MR spectroscopy system and method for diagnosing painful and non-painful intervertebral discs

ABSTRACT

An MR Spectroscopy (MRS) system and approach is provided for diagnosing painful and non-painful discs in chronic, severe low back pain patients (DDD-MRS). A DDD-MRS pulse sequence generates and acquires DDD-MRS spectra within intervertebral disc nuclei for later signal processing and diagnostic analysis. An interfacing DDD-MRS signal processor receives output signals of the DDD-MRS spectra acquired and is configured to optimize signal-to-noise ratio by an automated system that selectively conducts optimal channel selection, phase and frequency correction, and frame editing as appropriate for a given acquisition series. A diagnostic processor calculates a diagnostic value for the disc based upon a weighted factor set of criteria that uses MRS data extracted from the acquired and processed MRS spectra for multiple chemicals that have been correlated to painful vs. non-painful discs. A display provides an indication of results for analyzed discs as an overlay onto a MRI image of the lumbar spine.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.16/408,188, filed May 9, 2019, and titled “MR SPECTROSCOPY SYSTEM ANDMETHOD FOR DIAGNOSING PAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS,”which is a continuation of U.S. patent application Ser. No. 15/669,584,filed Aug. 4, 2017, and titled “MR SPECTROSCOPY SYSTEM AND METHOD FORDIAGNOSING PAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS,” which is acontinuation of U.S. patent application Ser. No. 14/872,986, filed Oct.1, 2015, and titled “MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSINGPAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS,” which is a continuationof U.S. patent application Ser. No. 14/310,683, filed Jun. 20, 2014, andtitled “MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSING PAINFUL ANDNON-PAINFUL INTERVERTEBRAL DISCS,” which is a continuation of U.S.patent application Ser. No. 13/444,731, filed Apr. 11, 2012, and titled“MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSING PAINFUL ANDNON-PAINFUL INTERVERTEBRAL DISCS,” which is a continuation ofInternational Patent Application No. PCT/US2010/052737, filed on Oct.14, 2010, and titled “MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSINGPAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS,” which designates theUnited States, and which is a continuation-in-part of U.S. patentapplication Ser. No. 12/579,371, filed Oct. 14, 2009, and titled “MRSPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSING PAINFUL AND NON-PAINFULINTERVERTEBRAL DISCS,” each of which is hereby incorporated by referencein its entirety and made a part of this specification for all that itdiscloses.

BACKGROUND OF THE INVENTION Field of the Invention

This disclosure relates to systems, processors, devices, and methods formeasuring chemical constituents in tissue for diagnosing medicalconditions. More specifically, it relates to systems, pulse sequences,signal and diagnostic processors, diagnostic displays, and relatedmethods using novel application of nuclear magnetic resonance, includingmagnetic resonance spectroscopy, for diagnosing pain such as low backpain associated with degenerative disc disease.

Description of the Related Art

While significant effort has been directed toward improving treatmentsfor discogenic back pain, relatively little has been done to improve thediagnosis of painful discs.

Magnetic resonance imaging (MRI) is the primary standard of diagnosticcare for back pain. An estimated ten million MRIs are done each year forspine, which is the single largest category of all MRIs at an estimated26% of all MRIs performed. MRI in the context of back pain is sensitiveto changes in disc and endplate hydration and structural morphology, andoften yields clinically relevant diagnoses such as in setting ofspondlyolesthesis and disc herniations with nerve root impingement (e.g.sciatica). In particular context of axial back pain, MRI is principallyuseful for indicating degree of disc degeneration. However, degree discdegeneration has not been well correlated to pain. In one regard, peoplefree of back pain often have disc degeneration profiles similar to thoseof people with chronic, severe axial back pain. In general, not alldegenerative discs are painful, and not all painful discs aredegenerative. Accordingly, the structural information provided bystandard MRI exams of the lumbar spine is not generally useful fordifferentiating between painful and non-painful degenerative discs inthe region as related to chronic, severe back pain.

Accordingly, a second line diagnostic exam called “provocativediscography” (PD) is often performed after MRI exams in order tolocalize painful discs. This approach uses a needle injection ofpressurized dye in awake patients in order to intentionally provokepain. The patient's subjective reporting of pain level experiencedduring the injection, on increasing scale of 0-10, and concordancy tousual sensation of pain, is the primary diagnostic data used todetermine diagnosis as a “positive discogram”—indicating painfuldisc—versus a “negative discogram” for a disc indicating it is not asource of the patient's chronic, severe back pain. This has significantlimitations including invasiveness, pain, risks of disc damage,subjectivity, lack of standardization of technique. PD has beenparticularly challenged for high “false+” rates alleged in variousstudies, although recent developments in the technique and studiesrelated thereto have alleged improved specificity of above 90%. (Wolferet al., Pain Physician 2008; 11:513-538, ISSN 1533-3159). However, thesignificant patient morbidity of the needle-based invasive procedure isnon-trivial, as the procedure itself causes severe pain and furthercompromises time from work. Furthermore, in another recent study PD wasshown to cause significant adverse effects to long term disc health,including significantly accelerating disc degeneration and herniationrates (on the lateral side of needle puncture). (Carragee et al., SPINEVolume 34, Number 21, pp. 2338-2345, 2009). Controversies around PDremain, and in many regards are only growing, despite the on-goingprevalence of the invasive, painful, subjective, harmful approach as thesecondary standard of care following MRI. PD is performed an estimated400,000 times annually world-wide, at an estimated total economic costthat exceeds $750 Million Dollars annually. The need for a non-invasive,painless, objective, non-significant risk, more efficient andcost-effective test to locate painful intervertebral discs of chronic,severe low back pain patients is urgent and growing.

A non-invasive radiographic technique to accurately differentiatebetween discs that are painful and non-painful may offer significantguidance in directing treatments and developing an evidence-basedapproach to the care of patients with lumbar degenerative disc disease(DDD).

SUMMARY OF THE INVENTION

One aspect of the present disclosure is a MRS pulse sequence configuredto generate and acquire a diagnostically useful MRS spectrum from avoxel located principally within an intervertebral disc of a patient.

Another aspect of the present disclosure is an MRS signal processor thatis configured to select a sub-set of multiple channel acquisitionsreceived contemporaneously from multiple parallel acquisition channels,respectively, of a multi-channel detector assembly during arepetitive-frame MRS pulse sequence series conducted on a region ofinterest within a body of a subject.

Another aspect of the present disclosure is an MRS signal processorcomprising a phase shift corrector configured to recognize and correctphase shifting within a repetitive multi-frame acquisition seriesacquired by a multi-channel detector assembly during an MRS pulsesequence series conducted on a region of interest within a body of asubject.

Another aspect of the present disclosure is a MRS signal processorcomprising a frequency shift corrector configured to recognize andcorrect frequency shifting between multiple acquisition frames of arepetitive multi-frame acquisition series acquired within an acquisitiondetector channel of a multi-channel detector assembly during a MRS pulsesequence series conducted on a region of interest within a body of asubject.

Another aspect of the present disclosure is a MRS signal processorcomprising a frame editor configured to recognize at least one poorquality acquisition frame, as determined against at least one thresholdcriterion, within an acquisition channel of a repetitive multi-frameacquisition series received from a multi-channel detector assemblyduring a MRS pulse sequence series conducted on a region of interestwithin a body of a subject.

Another aspect of the present disclosure is an MRS signal processor thatcomprises an apodizer to reduce the truncation effect on the sampledata. The apodizer can be configured to apodize an MRS acquisition framein the time domain otherwise generated and acquired by via an MRS aspectotherwise herein disclosed, and/or signal processed by one or more ofthe various MRS signal processor aspects also otherwise hereindisclosed.

Another aspect of the present disclosure is an MRS diagnostic processorconfigured to process information extracted from an MRS spectrum for aregion of interest in a body of a subject, and to provide the processedinformation in a manner that is useful for diagnosing a medicalcondition or chemical environment associated with the region ofinterest.

Another aspect of the present disclosure is an MRS system comprising anMRS pulse sequence, MRS signal processor, and MRS diagnostic processor,and which is configured to generate, acquire, and process an MRSspectrum representative of a region of interest in a body of a patientfor providing diagnostically useful information associated with theregion of interest.

Still further aspects of the present disclosure comprise various MRSmethod aspects associated with the other MRS system, sequence, andprocessor aspects described above.

Each of the foregoing aspects, modes, embodiments, variations, andfeatures noted above, and those noted elsewhere herein, is considered torepresent independent value for beneficial use, including even if onlyfor the purpose of providing as available for further combination withothers, and whereas their various combinations and sub-combinations asmay be made by one of ordinary skill based upon a thorough review ofthis disclosure in its entirety are further contemplated aspects also ofindependent value for beneficial use.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will now be described with reference to the drawings ofembodiments, which embodiments are intended to illustrate and not tolimit the disclosure.

FIGS. 1A-C show respective MRI images of an intervertebral disc regionof a lumbar spine with overlay features representing a voxelprescription within a disc for performing a DDD-MRS exam according toone aspect of the disclosure, in coronal, sagittal, and axial imagingplanes, respectively.

FIG. 2 shows an example of the sectional deployment in one commerciallyavailable MR spine detector coil assembly, and with which certainaspects of the present disclosure may be configured to interface forcooperative operation and use, and have been so configured and usedaccording to certain Examples provided elsewhere herein.

FIG. 3A shows an example of a CHESS water suppression pulse sequencediagram representing certain pulse sequence aspects contemplated bycertain aspects of the present disclosure.

FIG. 3B shows certain aspects of a combined CHESS-PRESS pulse sequencediagram also consistent with certain aspects of the present disclosure.

FIG. 3C shows various different aspects of a combined CHESS-VSS-PRESSpulse sequence diagram also illustrative of certain aspects of thepresent disclosure.

FIGS. 4A-B show two examples of respective planar views of a veryselective saturation (VSS) prescription for a voxelated acquisitionseries in an intervertebral disc to be conducted via a DDD-MRS pulsesequence according to further aspects herein.

FIG. 5 shows Real (Sx) and imaginary (Sy) parts of an FID (right) thatcorrespond to x and y components of the rotating magnetic moment M(left).

FIG. 6 shows an amplitude plot of complex data from a standard MRSseries acquisition of multiple frame repetitions typically acquiredaccording to certain present embodiments, and shows amplitude of signalon the y-axis and time on the x-axis.

FIG. 7 shows a graphical plot of an MRS absorption spectrum from an MRSpulse sequence acquisition from a lumbar disc using a 3T MR system, andwhich is produced from the transform of the complex data as the outputaverage after combining all of 6 activated acquisition channels andaveraging all frames, such as typically provided in display by acommercially available MRS system, and is generated without applying thevarious signal processing approaches of the present disclosure.

FIG. 8 shows a graphical display of individual channel MRS spectra ofall uncorrected channels of the same MRS acquisition featured in FIG. 7, and is shown as “real part squared” representation of the acquired MRSspectral data prior to combining the channels, and is also prior topre-processing according to the signal processing approaches of thepresent disclosure.

FIG. 9A shows a schematic flow diagram of one DDD-MRS processorconfiguration and processing flow therein, first operating in DDD-MRSsignal processor mode by conducting optimal channel (coil) selection,phase correcting, then apodizing, then transforming domain (from time tofrequency), then frame editing (editing out poor quality frames whileretaining higher quality), then frequency error correction (correctingfor frequency shifts), then averaging of all selected coils, and thenfollowed by a DDD-MRS diagnostic processor and processing flow thatcomprises data extraction related to MRS spectral regions of diagnosticinterest, then applying the diagnostic algorithm, then generating adiagnostic patient report.

FIG. 9B shows a schematic flow diagram of further detail of variouscomponent parts of the DDD-MRS signal processor and respective stepstaken thereby as shown more generally in FIG. 9A.

FIG. 9C shows a schematic flow diagram of further detail of variouscomponent parts of the DDD-MRS diagnostic processor and processing flowtaken thereby as also shown more generally in FIG. 9A.

FIG. 10 shows a plot of phase angle pre- and post-phase correction foran acquisition series example, and as is similarly applied for a DDD-MRSacquisition such as for a disc according to certain aspects of thepresent disclosure.

FIG. 11 shows the serial acquisition frame averages for each of 6individual acquisition channels as shown in FIG. 8 , but after phasecorrection consistent with the signal processing flow shown in FIGS.9A-B and phase-correction approach illustrated in FIG. 10 .

FIG. 12 shows the frame-averaged real part squared MRS spectrum aftercombining the strongest two channels (channels 1 and 2) selected amongthe 6 phase-corrected frame-averaged channel spectra shown in FIG. 11using a channel selection approach and criterion according to a furtheraspect of the current disclosure, but without frequency correction.

FIG. 13 shows an example of a time-intensity plot for a DDD-MRSacquisition similar to that shown in FIG. 17D for the acquisition shownin FIGS. 7-8 and 11-12 , except that the plot of FIG. 13 relates toanother MRS pulse sequence acquisition series of another lumbar disc inanother subject with corrupted frames midway along the temporalacquisition series in order to illustrate frame editing according toother aspects of the disclosure.

FIG. 14A shows confidence in frequency error estimate vs. MRS framestemporally acquired across an acquisition series for a disc, as plottedfor the DDD-MRS series acquisition shown in different view in FIG. 13 .

FIG. 14B shows a frame by frame frequency error estimate of theacquisition series featured in FIG. 14A.

FIG. 15 shows all 6 frame-averaged acquisition channels for the seriesacquisition conducted on the disc featured in FIGS. 13-14B, prior tocorrection.

FIG. 16A shows phase corrected, frequency corrected, but not frameedited spectral average combining all of acquired series frames forchannels 3 and 4 as combined after optimal channel selection, for thesame series acquisition featured in FIGS. 13-15 .

FIG. 16B shows phase corrected, frequency corrected, and frame editedspectral average combining the partial retained frames not edited outfrom the acquired series for channels 3 and 4 as combined after optimalchannel selection, also for the same series acquisition featured inFIGS. 13-15 .

FIG. 17A shows a 2-dimensional time-intensity plot similar to that shownin FIG. 13 , but for yet another DDD-MRS acquisition series of anotherdisc in another subject and to illustrate another mode of frame editingaspects of the present disclosure.

FIG. 17B shows a waterfall plot in 3-dimensions for the DDD-MRSacquisition series shown in FIG. 17A, and shows the chemical shiftspectrum as a running cumulative average at discrete points over time ofserial frames acquired, with spectral amplitude on the vertical axis.

FIG. 17C shows an average DDD-MRS spectrum across the full acquisitionseries shown in FIGS. 17A-B, without frame editing, and plots both phaseonly and phase+frequency corrected formats of the spectrum.

FIG. 17D shows a 2-dimensional time-intensity plot similar to that shownand for the same DDD-MRS acquisition series of FIG. 17A, but onlyreflecting retained frames after editing out other frames according tothe present aspect of the disclosure and referenced to FIGS. 17A-C.

FIG. 17E shows a similar waterfall plot of cumulative spectral averagesand for the same DDD-MRS acquisition series shown in FIG. 17B, butaccording to only the retained frames after frame editing as shown inFIG. 17D.

FIG. 17F shows a similar average DDD-MRS spectrum and for the sameacquisition series shown in FIG. 17C, but only for the retained framesafter frame editing as shown in various modes in FIGS. 17D-E.

FIGS. 18A-B show time-intensity plots of the same MRS series acquisitionfor the same disc featured in FIGS. 7-8 and 11-12 as pre- (FIG. 18A) andpost- (FIG. 18B) frequency correction according to a further aspect ofthe present disclosure, and shows each acquisition frame as a horizontalline along a horizontal frequency range with brightness indicatingsignal amplitude (bright white indicating higher amplitude, darkerindicating lower), and shows the series of related repetitive frames intemporal relationship stacked from top to bottom, e.g. top is timezero).

FIGS. 19A-B show the same respective time-intensity plots shown in FIG.19A (pre-) and FIG. 19B (post-) frequency correction, but in enhancedcontrast format.

FIG. 20 shows spectral plots for 6 frame-averaged acquisition channelsfor the same acquisition shown in FIGS. 7-8 and 11-12 , except postphase and frequency correction and prior to optimal channel selectionand/or combination channel averaging.

FIG. 21 shows a spectral plot for phase and frequency error correctedchannels 1 and 2 selected from FIG. 20 as averaged, according to afurther aspect of the disclosure.

FIG. 22 shows a bar graph of mean values, with standard deviation errorbars, of Visual Analog Scale (VAS) and Oswestry Disability Index (ODI)pain scores calculated for certain of the pain patients and asymptomaticvolunteers evaluated in a clinical study of Example 1 and conductedusing certain physical embodiments of a diagnostic system constructedaccording to various aspects of the present disclosure.

FIG. 23 shows a Receiver Operator Characteristic (ROC) curverepresenting the diagnostic results of the DDD-MRS diagnostic systemused in the clinical study of Example 1 with human subjects featured inpart in FIG. 22 , as compared against standard control diagnosticmeasures for presumed true diagnostic results for painful vs.non-painful discs.

FIG. 24 shows a partition analysis plot for cross-correlation of aportion of the clinical diagnostic results of the DDD-MRS system underthe same clinical study of Example 1 and also addressed in FIGS. 22-23 ,based on partitioning of the data at various limits attributed todifferent weighted factors used in the DDD-MRS diagnostic processor,with “x” data point plots for negative control discs and “o” data pointplots for positive control discs, also shows certain statistical resultsincluding correlation coefficient (R²).

FIG. 25A shows a scatter plot histogram of DDD-MRS diagnostic resultsfor each disc evaluated in the clinical study of Example 1 and alsoaddressed in FIGS. 22-24 , and shows the DDD-MRS results separately forpositive control (PC) discs (positive on provocative discography or“PD+”), negative control (NC) discs (negative on provocative discographyor “PD−”, plus discs from asymptomatic volunteers or “ASY”), PD− alone,and ASY alone.

FIG. 25B shows a bar graph of the same DDD-MRS diagnostic results shownin FIG. 25A across the same subject groups of Example 1, but shows themean values with standard deviation error bars for the data.

FIG. 26 shows a bar graph of presumed true and false binary “positive”and “negative” diagnostic results produced by the DDD-MRS system forpainful and non-painful disc diagnoses in the clinical study of Example1, as compared against standard control diagnostic measures across thepositive controls, negative controls (including sub-groups), and alldiscs evaluated in total in the study.

FIG. 27 shows diagnostic performance measures of Sensitivity,Specificity, Positive Predictive Value (PPV), Negative Predictive Value(NPV), and area under the curve (AUC) which in this case is equivalentto Global Performance Accuracy (GPA) for the DDD-MRS diagnostic resultsin the clinical study of Example 1.

FIG. 28 shows a bar graph comparing areas under the curve (AUC) per ROCanalysis of MRI alone (for prostate cancer diagnosis), MRI+ PROSE (MRSpackage for prostate cancer diagnosis), MRI alone (for discogenic backpain or DDD pain), and MRI+DDD-MRS (for discogenic back pain or DDDpain), with bold arrows showing relative impact of PROSE vs. DDD-MRS onAUC vs. MRI alone for the respective different applications andindications, with DDD-MRS results shown as provided under Example 1.

FIG. 29 shows positive predictive value (PPV) and negative predictivevalue (NPV) for MRI alone and for MRI+DDD-MRS (per Example 1 results),both as applied for diagnosing DDD pain, vs. standard control measuressuch as provocative discography.

FIG. 30A shows a plot of DDD-MRS algorithm output data for a series of 8L4-L5 lumbar discs in 8 asymptomatic human control subjects perclinically acquired and processed DDD-MRS exam under Example 1, andplots these results twice for each disc on first (1) and second (2)separate repeat scan dates in order to demonstrate repeatability of theDDD-MRS exam's diagnostic results.

FIG. 30B shows a plot of PG/LAAL ratio data for 3 discs per DDD-MRSpulse sequence and signal processing data of Example 1, and shows theclinically acquired results via 3T DDD-MRS exams of the discs in vivo inpain patients (y-axis) against acquired measurements for the samechemicals in the same disc material but flash frozen after surgicalremoval and using 11T HR-MAS spectroscopy.

FIG. 31A shows a digitized post-processed DDD-MRS spectrum (in phasereal power) as processed according to certain of the MRS signalprocessor aspects of the present disclosure, and certain calculated dataderived therefrom as developed and used for calculated signal-to-noiseratio (SNR) of the processed result, as taken across a sub-set ofsamples evaluated under Example 1.

FIG. 31B shows a digitized pre-processed DDD-MRS spectrum (absorption)as 6 channel spectral average without deploying the MRS signalprocessing aspects of the present disclosure (e.g. “pre-processing”),and certain calculated data derived therefrom as developed and used forcalculated signal-to-noise ratio (SNR) of the processed result.

FIG. 31C shows a scatter plot histogram of signal-to-noise ratio (SNR)for standard “all channels, non-corrected” frame averaged MRS spectra(absorption) produced by the 3T MR system for a subset of discsevaluated using the DDD-MRS pulse sequence in the clinical study ofExample 1, and the SNR of MRS spectra (in phase real power) for the sameseries acquisitions for the same discs post-processed by the DDD-MRSsignal processor configured according to various of the present aspectsof this disclosure, as such SNR data was derived for example asillustrated in FIGS. 31A-B.

FIG. 31D shows the same data shown in FIG. 31C, but as bar graph showingmean values and standard deviation error bars for the data within eachpre-processed and post-processed groups.

FIG. 31E shows a scatter plot histogram of the ratio of SNR valuescalculated post- versus pre-processing for each of the discs per the SNRdata shown in FIGS. 31C-D.

FIG. 31F shows a bar graph of mean value and standard deviation errorbar of the absolute difference between post- and pre-processed SNRvalues for each of the discs shown in different views in FIGS. 31C-E.

FIGS. 31G-H respectively show the mean and standard deviation forabsolute improvement between pre- and post-processed SNR (FIG. 31F), themean ratio improvement of post-processed/pre-processed SNR (FIG. 31G),and the mean % improvement of post-processed vs. pre-processed SNR (FIG.31H).

FIG. 32A shows a mid-sagittal T2-weighted MRI image of a patientevaluated under the clinical study of Example 1 and comparing thediagnostic results of the operating embodiment for DDD-MRS systemdeveloped according to various aspects herein against provocativediscography results for the same discs, and shows a number-coded (andalso may be color coded) diagnostic legend for the DDD-MRS results (onleft of image) and discogram legend (top right on image) with overlay ofthe DDD-MRS results and discogram results on discs evaluated in thepatient.

FIG. 32B shows a mid-sagittal T2-weighted MRI image of another patientevaluated under the clinical study of Example 1 and comparing thediagnostic results of the physical embodiment DDD-MRS system developedaccording to various aspects herein against provocative discographyresults for the same discs, and shows a number-coded (and also may becolor coded) diagnostic legend for the DDD-MRS results (on left ofimage) and discogram legend (top right on image) with overlay of theDDD-MRS results and discogram results on discs evaluated in the patient.

FIG. 33A shows a scatter plot histogram plot of DDD-MRS (or “Nociscan”)diagnostic results against control groups for various discs evaluated invivo according to the data set reviewed and processed under Example 2,as similarly shown for the data evaluated in Example 1 in FIG. 25A (plusthe further addition of certain additional information further providedas overlay to the graph and related to another aspect of data analysisapplied according to further aspects of the present disclosure underExample 2).

FIG. 33B shows another scatter plot histogram of another processed formof the DDD-MRS diagnostic results also shown in FIG. 33A and per Example2, after transformation of the DDD-MRS diagnostic algorithm results forthe discs into “% probability painful” assigned to each disc asdistributed across the positive (POS) and negative (NEG) control groupsub-populations shown.

FIG. 34A shows a scatter plot histogram of signal-to-noise ratio (SNR)for standard “all channels, non-corrected” frame averaged MRS spectra(absorption) produced by the 3T MR system for a subset of discsevaluated using the DDD-MRS pulse sequence and signal processor in theclinical study of Example 2, and the SNR of spectra (absorption) for thesame series acquisitions for the same discs post-processed by theDDD-MRS processor, as such SNR data was derived for example asillustrated in FIGS. 31A-B.

FIG. 34B shows the same data shown in FIG. 34A, but as bar graph showingmean values and standard deviation error bars for the data within eachpre-processed and post-processed groups.

FIG. 34C shows a scatter plot histogram of the ratio of SNR valuescalculated post- versus pre-processing for each discs per the SNR datashown in FIGS. 34A-B.

FIG. 34D shows a bar graph of mean value and standard deviation errorbar of the absolute difference between post- and pre-processed SNRvalues for each of the discs shown in different views in FIGS. 34A-C.

FIG. 34E shows a bar graph of mean value and standard deviation errorbar of the ratio of post- to pre-processed SNR values for each of thediscs shown in different views in FIGS. 34A-D.

FIG. 34F shows a bar graph of the mean value and standard deviationerror bar for the percent increase in SNR from pre- to post-processedMRS spectra for each of the discs further featured in FIGS. 34A-E.

FIG. 35 shows a DDD-MRS spectrum illustrative of a perceived potentiallipid signal contribution as overlaps with the regions otherwise alsoassociated with lactic acid or lactate (LA) and alanine (AL), accordingto further aspects of the present disclosure and as relates to Example3.

FIG. 36 shows a scatter plot histogram of DDD-MRS diagnostic algorithmresults for the test population of in vivo discs, as calculated for adefined Group A evaluated for diagnostic purposes via Formula A, underthe Example 3.

FIGS. 37A-C show scatter plot histogram of certain embodiments for theDDD-MRS diagnostic processor for discs designated as Group B underExample 3, including as shown with respect to PG/LAAL ratio results forthe discs (FIG. 17A), logistic regression generated Formula B resultsfor the discs (FIG. 17B), and the transformed % probability paindistribution for the same Group B discs as a result of the results inFIG. 17B (FIG. 17C).

FIG. 38 shows a scatter plot histogram of certain embodiments forDDD-MRS diagnostic processor for discs designated as Group C discs underExample 3, after applying logistic regression generated Formula C to theDDD-MRS spectral data acquired for the group of discs.

FIG. 39 shows a scatter plot histogram of another embodiment for DDD-MRSdiagnostic algorithm, as applied to Group C discs under Example 3according to a Formula B “hybrid” illustrative of yet a furtherembodiment of the present disclosure.

FIGS. 40A-B show MRI images of two lumbar spine phantoms according toanother Example 4 of the disclosure.

FIGS. 40C-D show graphical plots of n-acetyl (NAA) and lactic acid (LA)concentrations in discs from phantoms shown in FIGS. 40A-B as measuredaccording to certain DDD-MRS aspects of the present disclosure, versusknown amounts, per Example 4.

FIGS. 41A-B show schematic flow diagrams of a DDD-MRS exam, includingDDD-MRS pulse sequence, DDD-MRS signal processing, and DDD-MRS algorithmprocessing, and various data communication aspects, according to certainfurther aspects of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Previously reported lab experiments used 11T HR-MAS Spectroscopy tocompare chemical signatures of different types of ex vivo disc nucleiremoved at surgery. (Keshari et al., SPINE 2008) These studiesdemonstrated that certain chemicals in disc nuclei, e.g. lactic acid(LA) and proteoglycan (PG), may provide spectroscopically quantifiablemetabolic markers for discogenic back pain. This is consistent withother studies that suggest DDD pain is associated with poor discnutrition, anaerobic metabolism, lactic acid production (e.g. risingacidity), extracellular matrix degradation (e.g. reducing proteoglycan),and increased enervation in the painful disc nuclei. In many clinicalcontexts, ischemia and lowered pH cause pain, likely by provokingacid-sensing ion channels in nociceptor sensory neurons.

The previous disclosures evaluating surgically removed disc samples exvivo with magnetic resonance spectroscopy (MRS) in a laboratory settingis quite encouraging for providing useful diagnostic tool based on MRS.However, an urgent need remains for a reliable system and approach foracquiring MRS signatures of the chemical composition of theintervertebral discs in vivo in a readily adoptable clinicalenvironment, and to provide a useful, clinically relevant diagnostictool based on these acquired MRS signatures for accurately diagnosingdiscogenic back pain. A significant need would be met by replacing PDwith an alternative that, even if diagnostically equivalent, overcomesone or more of the significant shortcomings of the PD procedure by beingnon-invasive, objective, pain-free, risk-free, and/or morecost-effective. Magnetic resonance spectroscopy (MRS) is a medicaldiagnostic platform that has been previously developed and characterizedfor a number of applications in medicine. Some of these have beenapproved such as for example for brain tumors, breast cancer, andprostate cancer. Some MRS platforms disclosed have been multi-voxel, andothers single voxel. None of these have been adequately configured ordeveloped for in vivo clinical application to reliably diagnose medicalconditions or chemical environments associated with nociceptive pain,and/or with respect to intervertebral discs such as may be associatedwith disc degeneration and/or discogenic back pain (including inparticular, but without limitation, with respect to the lumbar spine).

Various technical approaches have also been alleged to enhance thequality of MRS acquisitions for certain purposes. However, theseapproaches are not considered generally sufficient to provide thedesired spectra of robust, reliable utility for many intervertebraldiscs in vivo, at least not at field strengths typically employed for invivo spectroscopy, e.g. from about 1.2 tesla (T) or about 1.5 T to about3.0 T or even up to about 7 T. Furthermore, while individual techniqueshave been disclosed for certain operations that might be conducted inprocessing a given signal for potentially improved signal:noise ratio(SNR), an MRS signal processor employing multiple steps providingsignificant MRS signal quality enhancement, in particular with respectto improved SNR for multi-channel single voxel pulse sequenceacquisitions, have yet to be sufficiently automated to provide robustutility for efficient, mainstream clinical use, such as in primaryradiological imaging centers without sophisticated MR spectroscopistsrequired to process and interpret MRS data. This is believed to begenerally the case as a shortcoming for many such in vivo MRS exams ingeneral. Such shortcomings have also been observed in particularrelation to the unique challenge of providing a robust MRS diagnosticsystem for diagnosing medical conditions or otherwise chemicalenvironments within relatively small voxels, areas of highsusceptibility artifact potential, and in particular with respect tounique challenges of performing MRS in voxels within intervertebraldiscs (including with further particularity, although without necessarylimitation, of the lumbar spine). In solving many of these challengesaccording to certain aspects of the present disclosure, such as thoseproviding particular utility for diagnosing discogenic low back painand/or chemical environments within discs, additional beneficialadvances have also been made that are also considered more broadlyapplicable to MRS in general, and as may become adapted for manyspecific applications, as are also herein disclosed.

Certain aspects of the current disclosure therefore relate to new andimproved system approaches, techniques, processors, and methods forconducting in vivo clinical magnetic resonance spectroscopy (MRS) onhuman intervertebral discs, in particular according to a highlybeneficial mode of this disclosure for using acquired MRS information todiagnose painful and/or non-painful discs associated with chronic,severe axial lumbar (or “low”) back pain associated with degenerateddisc disease (or “DDD pain”). For purpose of helpful clarity in thisdisclosure, the current aspects, modes, embodiments, variations, andfeatures disclosed with particular benefits for this purposed aregenerally assigned the label “DDD-MRS.” However, other descriptors maybe used interchangeably as would be apparent to one of ordinary skill incontext of the overall disclosure. It is also further contemplatedwithin the scope of this present disclosure that, while this disclosureis considered to provide particular benefit for use involving such humanintervertebral discs (and related medical indications and purposes), thenovel approaches herein described are also considered more broadly andapplicable to other regions of interest and tissues within the body of asubject, and various medical indications and purposes. For purpose ofillustration, such other regions and purposes may include, withoutlimitation: brain, breast, heart, prostate, GI tract, tumors,degeneration and/or pain, inflammation, neurologic disorders,alzheimers, etc.

Various aspects of this disclosure relate to highly beneficial advancesin each of three aspects, and their various combinations, useful inparticular for conducting a DDD-MRS exam: (1) MRS pulse sequence forgenerating and acquiring robust MRS spectra; (2) signal processorconfigured to improve signal-to-noise ratio (SNR) of the acquired MRSspectra; and (3) diagnostic processor configured to use information fromthe acquired and processed MRS spectra for diagnosing painful and/ornon-painful discs on which the MRS exam is conducted in a DDD painpatient.

Several configurations and techniques related to the DDD-MRS pulsesequence and signal processor have been created, developed, andevaluated for conducting 3T (or other suitable field strength) MRS onhuman intervertebral discs for diagnosing DDD pain. A novel “DDD” MRSpulse sequence was developed and evaluated for this purpose, and withcertain parameters specifically configured to allow robust applicationof the signal processor for optimal processed final signals in acooperative relationship between the pulse sequence and post-signalprocessing conducted. These approaches can be used, for example, with a3 Tesla (3T) “Signa” MR system commercially available from GeneralElectric (GE). Highly beneficial results have been observed using thecurrent disclosed application technologies on this particular MRplatform, as has been demonstrated for illustration according toExamples provided herein, and it is to be appreciated that applying thepresent aspects of this present disclosure in combination with this onesystem alone is considered to propose significant benefit to painmanagement in patients requiring diagnosis. Accordingly, various aspectsof the present disclosure are described by way of specific reference toconfigurations and/or modes of operation adapted for compatible use withthis specific MR system, and related interfacing components such asspine detector coils, in order to provide a thorough understanding ofthe disclosure. It is to be appreciated, however, that this is done forpurpose of providing useful examples, and though significant benefitsare contemplated per such specific example applications to that system,this is not intended to be necessarily so limited and with broader scopecontemplated. The current disclosure contemplates these aspects broadlyapplicable according to one of ordinary skill to a variety of MRplatforms commercially available that may be different suitable fieldstrengths or that may be developed by various different manufacturers,and as may be suitably adapted or modified to become compatible for usewith such different systems by one of ordinary skill (with sufficientaccess to operating controls of such system to achieve this). Variousnovel and beneficial aspects of this present disclosure are thusdescribed herein, as provided in certain regards under the Examples alsoherein disclosed.

A DDD-MRS sequence exam is conducted according to one example overviewdescription as follows. A single three dimensional “voxel,” typically arectangular volume, is prescribed by an operator at a control consul,using 3 imaging planes (mid-sagittal, coronal, axial) to define the“region of interest” (ROI) in the patient's body, such as shown in FIGS.1A-C, for MR excitation by the magnet and data acquisition by theacquisition channel/coils designated for the lumbar spine exam withinthe spine detector coil assembly. The DDD-MRS pulse sequence applies apulsing magnetic and radiofrequency to the ROI, which causes singleproton combinations in various chemicals within the ROI to resonate atdifferent “signature resonant frequencies” across a range. Theamplitudes of frequencies at various locations along this range areplotted along a curve as the MRS “spectrum” for the ROI. This is doneiteratively across multiple acquisitions for a given ROI, typicallyrepresenting over 50 acquisitions, often 100 or more acquisitions, andoften between about 200 and about 600 acquisitions, such as between 300and 400 acquisitions for a given exam of a ROI. One acquisition spectrumamong these iterations is called a “frame” for purpose of thisdisclosure, though other terms may be used as would be apparent to oneof ordinary skill. These multiple acquisitions are conducted in order toaverage their respective acquired spectra/frames to reduce theamplitudes of acquired signal components representing noise (typicallymore random or “incoherent” and thus reduced by averaging) while bettermaintaining the amplitudes of signal components representing targetresonant chemical frequencies of diagnostic interest in the ROI(typically repeatable and more “coherent” and thus not reduced byaveraging). By reducing noise while maintaining true target signal, orat least resulting in less relative signal reduction, this multipleserial frame averaging process is thus conducted for the primaryobjective to increase SNR. These acquisitions are also conducted atvarious acquisition channels selected at the detector coils, such as forexample 6 channels corresponding with the lumbar spine area of the coilassembly used in the Examples (where for example 2 coils may be combinedfor each channel).

The 3T MRI Signa system (“Signa” or “3T Signa”), in standard operationconducting one beneficial mode of DDD-MRS sequence evaluated (e.g.Examples provided herein), is believed to be configured to average allacquired frames across all acquisition channels to produce a singleaveraged MRS curve for the ROI. This unmodified approach has beenobserved, including according to the various Figures and Examplesprovided herein, to provide a relatively low signal/noise ratio, withlow confidence in many results regarding data extraction at spectralregions of diagnostic interest, such as for example and in particularregions associated with proteoglycan or “PG” (n-acetyl) and lactate orlactic acid (LA). Sources of potential error and noise inherent in thisimbedded signal acquisition and processing configuration of the typicalMR system, for example were observed in conducting the DDD-MRS pulsesequence such as according to the Examples. These various sources ofpotential error or signal-to-noise ratio (SNR) compromise weredetermined to be mostly correctable—either by altering certainstructures or protocols of coil, sequence, or data acquisition, or inpost-processing of otherwise standard protocols and structures used.Among these approaches, various post-acquisition signal processingapproaches were developed and observed to produce significantly improvedand highly favorable results using otherwise un-modified operationpre-processing. In particular, various improvements developed andapplied under the current post-signal processor disclosed herein havebeen observed to significantly improve signal quality and SNR.

Certain such improvements advanced under the post-signal processorconfigurations disclosed herein include embodiments related to thefollowing: (1) acquisition channel selection; (2) phase errorcorrection; (3) frequency error correction; (4) frame editing; and (5)apodization. These modules or steps are typically followed by channelaveraging to produce one resulting “processed” MRS spectrum, whenmultiple channels are retained throughout the processing (though oftenonly one channel may be retained). These may also be conducted invarious different respective orders, though as is elsewhere furtherdeveloped frame editing will typically precede frequency errorcorrection. For illustration, one particular order of these operationsemployed for producing the results illustrated in the Examples disclosedherein are provided as follows: (1) acquisition channel selection; (2)phase correction; (3) apodization; (4) frame editing; (5) frequencycorrection; and (6) averaging.

While any one of these signal processing operations is considered highlybeneficial, their combination has been observed to provide significantlyadvantageous results, and various sub-combinations between them may alsobe made for beneficial use and are also contemplated. Variousillustrative examples are elsewhere provided herein to illustratesources of error or “noise” observed, and corrections employed toimprove signal quality. Strong signals typically associated with normalhealthy discs were evaluated first to assess the signal processingapproach. Signals from the Signa that were considered more “challenged”for robust data processing and diagnostic use were evaluated for furtherdevelopment to evaluate if more robust metabolite signal can be elicitedfrom otherwise originally poor SNR signals from the Signa.

Additional description further developing these aspects according toadditional embodiments, and other aspects, is provided below.

Spine Detector Coil and Patient Positioning

A typical DDD-MRS exam according to the present embodiments will beconducted in an MR scanner in which the patient lies still in a supineposition with a spine detector coil underneath the patient's back andincluding the lower spine. While this scanner applies the magnetic andRF fields to the subject, the spine detector coil functions as anantenna to acquire signals from resonating molecules in the body. Theprimary source of MRS signals obtained from a Signa 3T MR scanner,according to the physical embodiments developed and evaluated in theExamples herein this disclosure, are from the GE HD CTL 456 Spine Coil.This is a “receive-only” coil with sixteen coils configured into eightchannels. Each channel contains a loop and saddle coil, and the channelsare paired into sections. For lumbar (and thoracic) spine coverage, suchas associated with lumbar DDD pain diagnosis, sections 4, 5, and 6 aretypically deployed to provide six individual channel signals, as shownfor example in FIG. 2 .

Defining the Voxel (Voxel Prescription)

Certain embodiments of this disclosure relates principally to “singlevoxel” MRS, where a single three dimensional region of interest (ROI) isdefined as a “voxel” (VOlumetric piXEL) for MRS excitation and dataacquisition. The spectroscopic voxel is selected based on T2-weightedhigh-resolution spine images acquired in the sagittal, coronal, andaxial planes, as shown for example in FIGS. 1A-C. The patient is placedinto the scanner in a supine position, head first. The axial spineimages acquired are often in a plane oriented with disc angle (e.g. maybe oblique) in order to better encompass the disc of interest. Thisvoxel is prescribed within a disc nucleus for purpose of using acquiredMRS spectral data to diagnose DDD pain, according to the presentpreferred embodiments. In general for DDD-MRS applications evaluatingdisc nucleus chemical constituents, the objective for voxel prescriptionis to capture as much of the nuclear volume as possible (e.g. maximizingmagnitude of relevant chemical signals acquired), while restricting thevoxel borders from capturing therewithin structures of the outer annulusor bordering vertebral body end-plates (the latter being a moresignificant consideration, where lipid contribution may be captured andmay shroud chemical spectral regions of interest such as lactate oralanine, as further developed elsewhere herein). In fact, the actualoperation may not exactly coincide with acquiring signal from onlywithin the voxel, and may include some bordering region contribution.Thus some degree of spacing between the borders and these structures isoften desired. These typical objectives may be more difficult to achievefor some disc anatomies than others, e.g. relatively obliquely angleddiscs. For example, L5-S1 may be particularly challenging because insome patients it can frequently be highly angulated, irregularly shaped,and collapsed as to disc height.

In certain voxel prescriptions, the thickness is limited by thescanner's ability to generate the magnetic gradient that defines theZ-axis (axial plane) dimension. For example, a minimum thickness limitis pre-set to 4 mm on the GE Signa 3T. While such pre-set limits ofinterfacing, cooperative equipment and related software may result inlimits on the current application's ability to function in thatenvironment outside of these limits, the broad aspects of the currentdisclosure should not be considered necessarily so limited in all cases,and functionality may flourish within other operating ranges perhapsthan those specifically indicated as examples herein, such as in caseswhere such other imparted limitations may be released.

These usual objectives and potential limitations in mind, typical voxeldimensions and volumes (Z-axis, X-axis, Y-axis, Vol) may be for example5 mm (thick) by 14 mm (width) by 16 mm (length), and 1.12 cc, though onemay vary any or all of these dimensions by operator prescription to suita particular anatomy or intended application. The Z-axis dimension istypically limited maximally by disc height (in order to exclude theend-plates, described further herein), and minimally by either the setminimum limitations of the particular MR scanner and/or per SAR safetyconsiderations, in many disc applications (such as specific indicationfor pain diagnosis or other assessment of disc chemistry describedherein). This Z-axis dimension will typically be about 3 mm to about 6mm (thick), more typically between about 4 mm to about 6 mm, and mosttypically will be suitable (and may be required to be, per anatomy)between about 4 mm to about 5 mm. The other dimensions are typicallylarger across the disc's plane, and may be for example between about 15mm to about 20 mm (width and/or length), as have been observed suitableranges for most observed cases (e.g. per the Examples herein). While thehigher dimension of these ranges is typically limited only by borderingtissues desirable to exclude, the opportunity for patient motion toalter the relative location of the target voxel relative to actualanatomy may dictate some degree of “spacing” from such borderingstructures to ensure exclusion. The smaller dimensions of the ranges aremore related to degraded signal quality that comes with excessivelysmall voxel volume, whereas signal amplitude will typically be directlyrelated to voxel dimension and volume. Accordingly, voxels within discswill generally provide robust results, at least with respect to signalquality, at volumes of at least about 0.5 cc, and in many cases at leastabout 0.75 cc or 1 cc. This typically will be limited by borderinganatomy to up to about 2 ccs, or in some less typical cases up to about3 ccs for exceptionally large discs. These voxel volume ranges willtypically be achieved with various combinations of the typical axisdimensions as also stated above.

Also according to the typical voxel prescription objectives andlimitations stated above, an initial prescription may not be appropriatefor achieving acceptable results, though this may not be known until asequence is begun to allow observation of acquired signal quality.Accordingly, further aspects of the present disclosure contemplate avoxel prescription protocol which prescribes a first prescription,monitors results (either during scan or after completion, or via a“pre-scan” routine for this purpose), and if a lipid signature or othersuspected signal degradation from expected results is observed,re-prescribe the voxel to avoid suspected source of contaminant (e.g.make the voxel smaller or adjust its dimensions, tilt, or location) andre-run an additional DDD-MRS acquisition series (retaining the signalconsidered more robust and with least suspected signal degradationsuspected to be voxel error). According to still a further mode, apre-set protocol for re-prescribing in such circumstances may definewhen to accept the result vs. continue re-trying. In one embodiment, thevoxel may be re-prescribed and acquisition series re-run once, orperhaps twice, and then the best result is to be accepted. It is to beappreciated, as with many technology platforms, that operator trainingand techniques in performing such user-dependent operations may berelevant to results, and optimal (or conversely sub-optimal) results maytrack skill levels and techniques used.

To further illustrate this current aspect of the present disclosure, theexample of a single voxel prescription according to the typical threeplanar slice images is shown in FIGS. 1A-1C as follows. Morespecifically, FIG. 1A shows a coronal view oriented aspect of the voxelprescription. FIG. 1B shows a sagittal view oriented aspect of the voxelprescription. FIG. 1C shows an axial view oriented aspect of the voxelprescription.

The “DDD” MRS Pulse Sequence—PRESS

The DDD-MRS pulse sequence according to one embodiment shares certainsimilarities, though with certain differences and modifications definedherein, with another MRS pulse sequence called “PROSE”. PROSE isprimarily intended for use for diagnosing prostate cancer, and isapproved for use and sale and available from GE on 1.5 T GE MR systems.The DDD-MRS pulse sequence of the present embodiments, and PROSE forfurther reference, employ a sequence approach called Point RESolvedSpectroscopy (PRESS). This involves a double spin echo sequence thatuses a 90° excitation pulse with two 180° slice selective refocusingradio frequency (RF) pulses, combined with 3D chemical shift imaging(CSI) phase encoding gradients to generate 3-D arrays of spectral dataor chemical shift images. Due to the small size, irregular shape, andthe high magnetic susceptibility present when doing disc spectroscopyfor DDD pain, the 3D phase encoding option available under PROSE is notan approach typically to be utilized under the current disclosed versionof DDD-MRS sequence, and single voxel spectra are acquired by thisversion embodiment of DDD-MRS pulse sequence. This unique relativeconfiguration for the DDD-MRS pulse sequence can be accomplished bysetting the user control variables (CVs) for the matrix acquisition sizeof each axis to 1 (e.g., in the event the option for other setting ismade available). Further aspects of pulse sequence approachescontemplated are disclosed elsewhere herein. It is to be appreciatedthat while the modified PRESS approach herein described is particularlybeneficial, other approaches may be taken for the pulse sequenceaccording to one of ordinary skill consistent with other aspects andobjectives herein described and without departing from the broad aspectsof intended scope herein.

Water and Lipid Signal Suppression—CHESS

In another sequence called “PROBE” also commercially available from GE,and which is a CSI sequence used for brain spectroscopy, the lipid/fatsignals are believed to be resolved through the use of long TE (144 ms)periods and 2 dimensional transformations (2DJ). These acquisition andsignal processing techniques are believed to be facilitated by the largevoxel volumes prescribed in the brain as well as the homogeneity of thebrain tissue resulting in relatively narrow spectral line widths. In theprostate region targeted by the different pulse sequence of PROSE,however, the voxel prescriptions are much smaller and it is oftenimpossible to place the voxel so as to assuredly exclude tissues thatcontain lipid/fat. Therefore, two water and lipid suppression approachesare available and may be used, if warranted, in the PROSE sequence:“BASING” and “SSRF” (Spectral Spatial Radio Frequency). An even morechallenging environment of bordering lipid and reduced homogeneity hasbeen observed with the current DDD pain application of the lumbarintervertebral discs where the current ROI within disc nuclei areclosely bordered by vertebral bodies with bone marrow rich in lipidcontent. However, due both to the desire to use short TE times (e.g. 28ms) for the current DDD pain application in lumbar spine, and the desireto observe MRS signatures of other chemicals within disc nuclei that mayoverlap with lipid signal contribution along the relevant DDD-MRSspectrum, these water/lipid suppression approaches as developed forbrain and prostate application are not necessarily optimized for DDD-MRSapplication in many circumstances. While a SSRF suppression approach forlipid resonances may be employed in the DDD-MRS sequence, the narrowband RF pulse required for this may require a long RF period andamplitude that will exceed the SAR level for many MR systems.

Water suppression is also provided by a CHESS sequence interleaved orotherwise combined in some manner with the PRESS sequence in order toprovide appropriate results. Optimization of the residual water spectralline for frequency correction is done, according to one highlybeneficial further aspect of the present disclosure, with the settingprescribed for the third flip angle. The angle is lowered to reduce thewater suppression function which increases the residual water spectralline amplitude. Conversely higher relative third flip angles willincrease water suppression for reduced water signal in an acquired MRSspectrum. A particular flip angle for this purpose may be for exampleabout 125, though may be according to other examples between about 45degrees and about 125 degrees (or as much as about 145 degrees).Accordingly, in one aspect, the flip angle may be for example at leastabout 45 degrees. In another aspect, the flip angle may be up to 125degrees or even up to 145 degrees. Notwithstanding these examples, anexpanded experimental data set of 79 discs in 42 subjects representedunder Example 3 included robust, reliable results across this populationwith an average flip angle of about 120 or 121 degrees+/−about 33degrees. Moreover, a later component of that population conducted withfurther refinements revealed a majority of cases suitable at a flipangle between about 65 degrees and about 125 degrees, and in fact withmost found to be sufficient at about 85 degrees. It is to be appreciatedthat despite these specific number and range examples, and robustresults observed therefrom, it is also believed that flip angles withinabout 5 or 10 degrees apart are likely to produce susbstantially similarresults for purposes intended herein.

This flip angle aspect of the present disclosure is another examplewhere some degree of customization may be required, in order to optimizewater signal for a given disc, in a given particular MR system. As somediscs may be more dehydrated or conversely more hydrated than others,the water suppression may be more appropriate at one level for one disc,and at another level for another disc. This may require some iterativesetting and acquisition protocol to optimize, whereas the angle exampledescribed herein is considered appropriate for most circumstances andmay be a pre-defined starting place for “first try.”

For further clarity and understanding of the present DDD-MRS pulsesequence embodiments introduced above and also elsewhere hereindescribed, FIG. 3A shows an example of a CHESS water suppression pulsesequence diagram, whereas FIG. 3B shows an example of a combinedCHESS-PRESS pulse sequence diagram.

Very Selective Saturation (VSS) Bands

The volume excitation achieved using PRESS takes advantage of threeorthogonal slices in the form of a double spin echo to select a specificregion of interest. In some embodiments, the range of chemical shiftfrequencies (over 400 Hz for proton at 3.0 T) is not insignificantrelative to the limited band width of most excitation pulses (1000-2000Hz). The result can be a misregistration of the volume of interest forchemical shift frequencies not at the transmitter frequency. Thus, whena PRESS volume is resolved by MRS, the chemical levels may be not onlydependent on tissue level, T1 and T2, but also dependent on locationwithin the volume of interest. In some embodiments, due to imperfectionsin the RF pulse, out of volume excitation may occur which can presentsignals from chemicals that are not in the frequency/location range ofinterest.

Accordingly, another feature that is contemplated according to a furthermode of the DDD-MRS sequence is the use of very selective saturation(VSS) pulses. This is often beneficial to deploy for example for removalof signal contamination that may arise from chemical shift error due tothe presence of lipids within the voxel as well as outside the selectedROI or voxel in the disc nuclei. In the default operating mode of oneDDD-MRS sequence approach, in some regards sharing some similaritieswith PROSE, for example, multiple pairs of VSS RF suppression bands areplaced symmetrically around the prescribed DDD-MRS voxel. In certainembodiments, the voxel in this approach is oversized, such as forexample by 120% (e.g. PRESS correction factor=about 1.2). The DDD-MRSsequence according to this mode uses the VSS bands to define the actualDDD-MRS volume. It is believed that up to about six additional VSS bandsmay be prescribed (each consisting of three VSS RF pulses) graphicallyin PROSE, with the goal of reducing the chemical shift error that canoccur within the voxel as well as suppress excitation of out of voxeltissue during the PRESS localization of the voxel. According to someobservations in applying DDD-MRS to disc spectroscopy, these additionalgraphic VSS pulses were found to not significantly improve the volumeselection. In other observations, some benefit is suspected to haveresulted. Accordingly, while they may provide benefit in certaincircumstances, they also may not be necessary or even desired to be usedin others.

FIG. 3C shows a schematic diagram of certain aspects of a combinedCHESS-VSS-PRESS pulse sequence diagram also consistent with certainaspects of the present disclosure. As also shown for furtherillustration in multiple respective image planes in FIGS. 4A-B, multipleVSS bands are placed around the voxel prescription in each plane toreduce out of voxel excitation and chemical shift error present duringthe PRESS localization of the voxel.

PRESS Timing Parameters

For purpose of comparative reference, the echo time (TE) of about 130 msis believed to be the default selection typically used for PROSE dataacquisitions. This echo time is typically considered too long forDDD-MRS pulse sequence applications for acquiring robust disc spectradue to the small voxel volume and shorter T2 relaxation times of thechemical constituents of lumbar intervertebral discs, leading to adramatic decrease in signal to noise in long echo PRESS spectra.Therefore a shorter echo time setting for the scanner, such as forexample about 28 milliseconds, is generally considered more appropriateand beneficial for use in the current DDD-MRS sequence and DDD painapplication (though this may be varied as would be apparent to one ofordinary skill based upon review of this total disclosure and to suit aparticular situation). A frame repetition time (TR) of for example about1000 ms provides sufficient relaxation of the magnetic dipoles in theROI and leads to reasonable acquisition times and is believed torepresent a beneficial compromise between short acquisition times andsignal saturation at shorter values of TR (though this may also bevaried, as also elsewhere herein described). Other appropriatelyapplicable operating parameter settings for PRESS spectra suitablyapplicable to the DDD-MRS sequence may be, for example: number of datapoints equal to about 1024, number of repetitions equal to about 300,and example typical voxel size of about 4 mm×18 mm×16 mm (1.12 cc).Furthermore, first, second, and third flip angles of PRESS for thecurrent DDD-MRS sequence embodiment may be for example 90, 167, and 167,respectively (though these may slightly vary, and user-defined settingsmay not always reflect actual angle—for example the latter two valuesmay be exchanged with or represent one example of an actual result of a180 degree setting).

Summary of User Control Variable (CV) Examples for DDD-MRS Sequence

The foregoing disclosure describes various user controllable sequencesettings observed to be appropriate and of particular benefit for use inan example DDD-MRS sequence according to the current disclosure and foruse for diagnosing DDD pain, as contemplated under the preferredembodiments herein. These are further summarized in Table 1 appendedherewith at the end of this disclosure.

One or more of these CVs may comprise modifications from similarsettings that may be provided for another CHESS-PRESS or CHESS-VSS-PRESSpulse sequence, such as for example PROSE, either as defaults or as userdefined settings for a particular other application than as featured inthe various aspects herein this disclosure. These CV settings, incontext of use as modifications generally to a sequence otherwisesharing significant similarities to PROSE, are believed to result in ahighly beneficial resulting DDD-MRS sequence for the intended purpose oflater signal processing, according to the DDD-MRS signal processorembodiments herein described, and performing a diagnosis of DDD pain indiscs examined (the latter according for example to the DDD-MRSdiagnostic processor aspects and embodiments also herein disclosed).However, it is also appreciated that these specific settings may bemodified by one of ordinary skill and still provide highly beneficialresults, and are also contemplated within the broad intended scope ofthe various aspects of this present disclosure.

Data Acquisition of DDD-MRS Pulse Sequence

The signal detected in the MR spectrometer in the receiving “detector”coil assembly, after exposing a sample to a radio frequency pulse, iscalled the Free Induction Decay (FID) for purpose of this disclosure. Inmodern MR spectrometers the MR signal is typically detected usingquadrature detection. As a result, the acquired MR signal is composed oftwo parts, often referred as real and imaginary parts of the FID. Aschematic example of the time domain FID waveform is shown in FIG. 5 ,which shows the real (Sx) and imaginary (Sy) parts of an FID (right)that correspond to x and y components of the rotating magnetic moment M(left).

FIDs are generated at the period defined by TR. Thus a TR of about 1000milliseconds, according to the example embodiment described above,equals a rate of about 1 Hz (about one FID per second). The FID signalreceived from each coil channel is digitized by the scanner to generatea 1024 point complex number data set or acquisition frame. An MRS scansession consists of a number of frames of unsuppressed water FIDs (suchas for example may be about 16 frames) and up to 368 or more (as may bedefined by an operator or setting in the pulse sequence) frames ofsuppressed water FIDs, which together are considered an acquisitionseries. The unsuppressed water FIDs provide a strong water signal thatis used by the signal processing to determine which coils to use in thesignal processing scheme as well as the phase information from each coil(and in certain embodiments may also be used for frequency errorcorrection). However, due to gain and dynamic range in the system thesehigh water content unsuppressed frames do not typically provideappropriate resolution in the target biomarker regions of the associatedspectra to use them for diagnostic data purposes. The suppressed waterFIDs are processed by the DDD-MRS processor to obtain this spectralinformation, although the unsuppressed frames may be used for certainprocessing approaches taken by the processor.

For further illustration, FIG. 6 shows a plot of all the FIDs obtainedin a DDD-MRS pulse sequence scan according to certain presentillustrative embodiments, and is an amplitude plot of complex data froma standard DDD-MRS acquisition with the y-axis representing themagnitude of FID data and the x-axis representing serial frame countover time.

DDD-MRS Pulse Sequence Data Transfer from MR System to Post-Processor

The MR scanner generates the FIDs using the defined sequences toenergize the volume of interest (VOI), digitizes them according to thedefined data acquisition parameters, and stores the data, typically asfloating point numbers. While this data may be packaged, e.g. in“archive file,” and communicated in various formats and methods, oneexample is provided here. A data descriptor header file (DDF) with allthe aforementioned parameters along with voxel prescription data isappended to the data to generate the archive file. Examples of certainparameters provided in a DDF, are as follows: studyID (String);seriesNum (Integer for assigned Series Number); studyDate (String datecode); seriesDesc (String for series description); rootName (String);nSamps (Integer for number of complex samples, typically 1024); nFrames(Integer for number of frames or reps); coilName (String); pulseSeqName(String); Te (Float, echo time, in ms); Tr (Float, repetition time, inms); TxFreq (Float, in MHz); nSatBands (Integer, number of saturationbands); voxTilt (Float, voxel tilt about x-axis, in degrees); voxVol(Float, Voxel volume in cc); voxX (Float, Voxel X dimension, in mm);voxY (Float, Voxel Y dimension, in mm); voxZ (Float, Voxel Z dimension,in mm). The archive file can include data received from the MR scannerthat is representative of the anatomy of a patient (e.g., representativeof the chemical makeup of tissue inside the area of interest inside anintervertebral disc of the patient's spine).

The archive file may then be transferred to another computer running anapplication written in a language, such as for example Matlab® R2009a(e.g. with “Image Processing Toolbox” option, such as to generatetime-intensity plots such as shown in various Figures herein), whichopens the archive file. The Matlab application may be user-configurable,or may be configured as full or partial executables, and is configuredto signal process the acquired and transferred DDD-MRS informationcontained in the archive file, such as according to the various signalprocessing embodiments herein. Other software packages, such as “C,”“C+,” or “C++” may be suitably employed for similar purposes. Thisapplication, subsequently referred to as the DDD-MRS signal processor,parses information pertinent to the signal processing of the data fromthe data description header, and imports the FID data acquired at eachdetector coil for subsequent signal processing. It will be understoodthat the DDD-MRS signal processor can be implemented in a variety ofmanners, such as using computer hardware, firmware, or software, or somecombination thereof. In some embodiments, the DDD-MRS signal processorcan comprise a computer processor configured to execute a softwareapplication as computer-executable code stored in a non-transitorycomputer-readable medium. In some embodiments, the computer processorcan be part of a general purpose computer. In some embodiments, theDDD-MRS signal processor can be implemented using specialized computerhardware such as integrated circuits instead of computer software. Itwill be understood that the DDD-MRS signal processor, as well as othercomponents described herein that may be implemented by a computer, canbe implemented by multiple computers connected, for example, by anetwork or the internet. Thus, algorithms, processes, sequences,calculations, and tasks, etc. described herein can be divided intoportions to be performed by multiple computer processors or otherhardware located on multiple physically distinct computers. Also, sometasks that are described herein as being performed by distinct computersor systems may be performed by a single computer or a single integratedsystem.

The archive file and related MRS data may be communicated via a numberof available networks or methods to external source for receipt,processing, or other form of use. In one particular typical format andmethod, the information is communicated via picture archiving andcommunication system (PACS) that has become ubiquitous for storing andcommunicating radiologic imaging information. In addition to the archivefile with DDF and stored MRS data, accompanying MRI images may also bestored and communicated therewith, e.g. in standardized “digital imagingand communications in medicine” or “DICOM” format.

The data transfer described may be to a local computer processor forprocessing, or more remotely such as via the web (typically in secureformat). In alternative to data transfer of acquired MRS datapre-processing to an external system for post-processing as describedabove, e.g. MRS signal processor and diagnostic processor aspects ofthis disclosure, all or a portion of the various aspects of the presentembodiments may be installed or otherwise integrated within the MRsystem itself, e.g. a computer based controller or processor embeddedtherewithin or otherwise connected thereto, for operation prior topackaging results for output (and any remaining portions might beperformed peripherally or more remotely).

DDD-MRS Signal Processing

Upon the acquisition of all MRS data from a DDD-MRS pulse sequence exam,according to certain aspects of the present embodiments, the MR scannersystem will typically provide the operator with a spectral image that isthe averaged combination of all frames across all the 6 detectionchannels (coils). An example of such a waveform from an MRS pulsesequence exam acquired for an ROI in a disc nucleus via a GE Signa 3T MRsystem is shown in FIG. 7 , which shows a typical scanner-processedspectral signal plot of combined, averaged channels. FIG. 8 shows themagnitude only (no correction) MRS spectral images of each of the sixchannels which are aggregated to form the output from the example MRsystem as shown in FIG. 7 , and thus this raw uncorrected individualchannel spectral data output provides the input to the DDD-MRS signalprocessor of the present embodiments.

According to one highly beneficial mode, the DDD-MRS signal processor isconfigured to conduct a series of operations in temporal fashion asdescribed herein, and as shown according to the present detailedembodiments in the flow charts illustrated in general to increasingdetail for the various component modes and operable steps in FIGS. 9A-C.More specifically, FIG. 9A shows a general schematic overview for theflow of a diagnostic system 2 that includes a signal processor 4 and adiagnostic processor 6. Signal processor includes various sub-componentsand processors that carry out certain steps, such as a channel selectorthat conducts channel or “coil” selection step 10, phase corrector thatdoes phase correction step 20, apodizer that conducts apodizer step 30,domain transformer that conducts the domain transform step 44 such asfrom time domain to frequency domain, frame editor that conducts frameediting steps 50, frequency corrector that conducts frequency correctionsteps 60, and channel combiner that conducts combining or averagingsteps 70 to aggregate retained channels into one final post-processedspectral results (not shown). Following signal processing steps 6, adiagnostic processor conducts diagnostic processing of the signalprocessed signals through data extraction steps 110, diagnosticalgorithm application steps 120, and patient or diagnostic reportgeneration 130. While this configuration is considered highlybeneficial, these same or similar tasks may be performed in differentorder, as would be apparent to one of ordinary skill.

For illustration, FIGS. 9B-C show further details regarding some ofthese specific steps, and also illustrate a different order than hasbeen shown and referenced to FIG. 9A. More specifically, the signalprocessor 4 in shown to include the main primary steps shown in FIG. 9A,but in finer detail and different order. It some embodiments, the stepsshown in FIGS. 9A-C may be performed in an order different than thoseshown in FIGS. 9A-C. Also, in some embodiments, steps that are shown inFIGS. 9A-C can be omitted, combined with other steps, or divided intoadditional sub-steps. Additionally, in some cases, additional steps notspecifically shown in FIGS. 9A-C can be performed in addition to thesteps shown in FIGS. 9A-C.

Channel selection includes the following steps: signal power measurementstep 11 measures signal power for SNR calculation, as shown here in thespecific embodiment in first 100 points of FID with unsuppressed water.Noise power measurement step 13 measures noise in the last 100 points,for example, of the FID with unsuppressed frames. SNR estimate 15 isthen conducted, at which point thereafter channel selection step 17 isconducted per the channel with the maximum or highest signal. Channelselection includes an additional step 18 where additional channels areselected if within range of the strongest, e.g. about 3 dB. Uponcompleting channel selection, an index of selected channels is generated(step 19).

Frame editing operation 50 is also shown, with transformation ofunsuppressed water frame to frequency domain 51, locate water peak in+/−40 Hz per peak location step 53, frame confidence level calculation55, frame frequency error and store 57, and actual frame selection step59 based upon minimum confidence level threshold (e.g. 0.8) Phasecorrection 20 is also done per applying 21 1^(st) order linear curve fitto the FID of each unsuppressed water frame (e.g. n=16), obtainingaverage of zero order terms from the curve fit 23, and rotate 25 allsuppressed water frame FIDs by zero order term. Apodization 30 includesfor each selected channel and each frame indexed for frequencycorrection 31, then apply 250 point boxcar function to the FID (step33). In addition, frequency correction 60 entails for each selectedchannel and each frame indexed for frequency correction and apodization61, transform 63 the frame (FID) to the frequency of domain, and locatethe frequency error 65 for the frame as identified during frame editing.The spectrum is shifted 67 by the frequency error value to frequencycorrect the spectrum. Step 69 adds frequency corrected spectrum to manyto spectral average for all selected channels.

As also shown in FIG. 9C for the diagnostic processor 6, data extraction110 involves opening 111 the acquisition spectral average lot generatedin frequency correction, scan step 113 of spectral plot for metabolitefeatures and apply to bins, and record data bins to acquisitionmetabolite 115. The diagnostic algorithm 120 itself involves opening theacquisition metabolite file extracted from spectral average 121, extract123 metabolite features required by diagnostic linear regressionequations, and generate 125 acquisition diagnostic score and store tofile. Report generation 130 includes open acquisition diagnostic scorefill 131, open DICOM file 133 for Patient ID associated with acquisitiondiagnostic score, extract 135 sagittal image from the DICOM report,apply 137 acquisition score to sagittal image for each scanned disc, andreturn sagittal image to DICOM report 139.

According to the current example embodiment, a first operation of theDDD-MRS processor assesses the SNR of each channel. This is done todetermine which channels have acquired sufficiently robust signal to usefor data processing and averaging. The result may produce one singlechannel that is further processed, or multiple channels that are laterused in combination under multi-channel averaging. In the majority ofacquired signals observed according to the Examples disclosed herein,only a subset of the 6 lumbar acquisition channels were determined to besufficiently robust for use. However, the standard system outputaverages all 6 channels. Accordingly, this filtering processalone—removing poor signal channels and working with only strongersignal channels—has been observed to dramatically improve processedspectra for diagnostic use in some cases. While various techniques maybe suitable according to one of ordinary skill, and thus contemplatedherein, according to the present illustrative embodiment the SNR iscalculated by obtaining the average power in the first 100 data points(the signal) and the last 100 points (the noise) of the unsuppressedwater FID. The unsuppressed water FIDs signals are used because of thestrong water signal. The channel with the greatest SNR, and channelswithin a predetermined threshold variance of that strongest one, e.g.within about 3 dB for example, are preserved for further processing andas candidates for multi-channel averaging—other channels falling belowthis range are removed from further processing (though may be used forfurther processing, yet removed from final results).

Further examples and embodiments for evaluating relative channel qualityare provided as follows. One additional indication of channel qualitythat may be observed and used is the line width of the unsuppressedwater signal based on the averaged frequency and phase corrected FFTs ofthe coil channels with the highest SNRs. This is computed to serve as ageneral indicator of signal quality as determined by the quality of theshimming process and to provide an estimate of the resolution we shouldexpect in the chemical shift spectrum. Another indication of channelquality is the degree of water suppression. This has utility indetermining the optimum degree of water suppression to apply in theacquisition protocol. The water suppression should leave enough residualwater signal to use as a reference to reliably perform frame-by-framefrequency correction but not so much that water signal artifacts affectthe chemical shift spectrum in the metabolite areas of interest. Suchartifacts include simple spectral leakage as well as phase modulationsidebands due to gradient vibration induced B_(o) modulation.

Further to the present embodiments and per further reference to FIGS.9A-B, a second operation conducted by the DDD-MRS processor is phasealignment, or phase error correction. This is performed to supportcoherent summation of the signals from the selected channels and theextraction of the absorption spectra. This is often necessary, or atleast helpful, because in many cases a systemic phase bias is present inthe different channels. This systemic phase bias is best estimated byanalysis of the data frames (e.g. about 16 frames in the DDD-MRS pulsesequence of the current illustrative detailed embodiments) collected atthe beginning of each scan without water suppression. This operation,according to one mode for example, analyzes the phase sequence of thecomplex samples and fits a polynomial to that sequence. A first-order(linear) fit is used in one further illustrative embodiment. This isbelieved to provide a better estimate of the offset than simply usingthe phase of the first sample, as is often done. This is because eddycurrent artifacts, if present, will be most prominent in the first partof the frame. The offset of the linear fit is the initial phase.Observation has indicated that the first 150 samples (75 mS at thetypical 2000 samples-per-second rate) typically provide reliable phasedata. The fit is performed on each of the water-unsuppressed frames foreach channel and the mean phase of these is used to phase adjust thedata for the corresponding channel. This is accomplished by performing aphase rotation of every complex sample in each frame to compensate forthe phase offset as estimated above, setting the initial phase to zero.

The offset of the linear fit is the phase bias with respect to zero andthe slope is the frequency error with respect to perfect center-tuningon the water signal. Only the offset portion of the curve fit is used tophase correct the data. An illustrative example of this is shown inschematic form in FIG. 10 , which shows phase angle before and afterphase correction. The phase angle signal is shown as the dotted line.The solid line is the least squares fit estimate. The dashed line is thephase and frequency corrected signal, though the offset component isused to phase correct and frequency correction is performed subsequentlyin the temporal process according to the present DDD-MRS processorembodiment.

The real-part squared MRS spectral results of phase correction for eachof all the six channels shown prior to correction in FIG. 8 is shown inFIG. 11 , with channels 1-3 indicated from left to right at the top, andchannels 4-6 indicated from left to right at the bottom of the figure.The averaged spectrum of the selected, phase corrected channels(channels 1 and 2) is shown in FIG. 12 , which reflects significant SNRimprovement versus the uncorrected all channel average spectrum shown inFIG. 7 .

Frame Editing

While it is contemplated that in some circumstances individual MRSacquisition frames may provide some useful information, frame averagingis prevalently indicated in the vast majority of cases to achieve aspectrum with sufficient SNR and interpretable signal at regions ofinterest for pathology assessment. It is, at most, quite rare that anindividual frame will have sufficient SNR for even rudimentarymetabolite analysis to the extent providing reliable diagnosticinformation. Often individual frames along an acquisition series willhave such low SNR, or possess such artifacts, that they make noimprovement to the average—and in fact may even degrade it. To theextent these “rogue” frames may be recognized as such, they may beexcluded from further processing—with only robust frames remaining, theresult should improve.

Accordingly, a further mode of the present DDD-MRS processor embodimentutilizes a frame editor to conduct frame editing to identify thoseframes which vary sufficiently from the expected or otherwise observedacquisition results such that they should be excluded, as is alsorepresented schematically in the flow diagram examples of FIGS. 9A-B. Inone aspect of the underlying concern, certain patient motions during anacquisition may result in signal drop-out as well as frequency shifts(e.g. magnetic susceptibility artifact). While involuntary motion, e.g.respiration, is a common cause of frequency shifts, these are typicallysufficiently minor and within a range that they are not believed toimplicate signal quality other than the shift itself (which can besignificant source of SNR degradation, but correctable per the presentdisclosure). However, other more significant movements (e.g. voluntary)may cause sufficiently significant shifts to seriously degrade theacquired spectrum, beyond merely correctable spectral shifts. Forexample, such activity may move the voxelated region to include adjacenttissues versus only the intended VOI upon prescription prior to themotion. If the salient artifact is frequency shift, a correction may beapplied and the frame can be used to make a positive contribution to theaveraged spectrum. If a frame is discarded its contribution is lost, andacross sufficient number of discarded frames across a series the resultmay not include a sufficient number of frames in the average for areliable SNR in the resulting spectrum. The DDD-MRS processor, accordingto the current embodiment, analyzes the residual water signal in eachframe to determine if it is of sufficient quality to support frequencycorrection.

FIG. 13 shows a time-intensity plot which illustrates a scan series withfrequency shifts and “drop outs” with SNR changes considered torepresent corrupted frames due to patient motion. More specifically,this shows 1 dimensional horizontal lines for each frame, with signalamplitude reflected in “brightness” or intensity (e.g. higher values arewhiter, lower are darker), with time across the serial acquisition ofthe series progressing top to bottom vertically in the Figure. Avertical band of brightness is revealed to the left side of the plot.However, in this particular example, there is a clear break in this bandas “drop out” frames. After excluding the “drop out” frames (center oftime sequence between about 75 and 175 MRS frames, it was still possibleto obtain a high quality final averaged spectrum from this scan usingthe remaining robust frames, as further developed immediately below.

FIGS. 14A and 14B show the confidence level estimate and the frame byframe frequency error estimate, respectively, which are used accordingto the present embodiment for frame editing according to thisacquisition series example of FIG. 13 . More specifically, FIG. 14Ashows the frame by frame confidence level, with confidence level on theY-axis, and the sequential series of frame acquisitions along a scanindicated along the X-axis. FIG. 14B shows the actual frequency erroralong the Y-axis, for the same frame series along the X-axis. This isbased on analyzing the characteristics of the residual water peak andthe noise in a band 80 Hz wide (for 3T processing, the band would be 40Hz wide at 1.5 T) around the center-tuned frequency. The largest peak isassumed to be the water signal and the assumption is qualified by theconfidence estimate. For the purpose of this example, if the confidencevalue is above a threshold, i.e. 0.8, the frame is flagged as acandidate for frequency correction and thus “retained.” As seen from theplots in FIGS. 14A-B, when the confidence is low, the variance of thefrequency error estimate is greatly increased. The final qualificationstep, per this example, is to determine if there are enough qualifiedcandidate frames to achieve sufficient SNR improvement when averaged.This threshold limit for proceeding with frequency correction (and thusframe editing therefore) has been empirically established as 90 framesmeeting the criteria. According to the present embodiments, this hasbeen observed to provide sufficiently robust results per the Examplesdescribed herein. It is to be appreciated, however, that other limitsmay be appropriate in various circumstances. The number of framesrequired will be based upon the SNR levels achievable from the completedsignal processing. This will be paced by SNR of input signalacquisitions to begin with, and performance of other signal processingmodes and steps taken with those signals. According to the acquisitionsunder the Examples disclosed herein, SNR is believed to increase overabout 150 frames, and then with little gained typically thereafter,though the 90 frame minimum limit has been observed to providesufficient results when reached (in rare circumstances). In the eventthe result drops below the 90 frame limit, the DDD-MRS processor isstill configured to proceed with other modes of signal processing,signal quality evaluation, and then diagnostic processor may be stillemployed—just without the added benefit of the frame editing andfrequency error correction.

Further description related to acceptable confidence level estimateapproach according to the present disclosure is provided as follows, forfurther illustration of this embodiment for the frame editing andfrequency correction modes of the disclosure. The discrete amplitudespectrum can be analyzed in the range of the center-tuned frequency ±40Hz for example in the case of a 3T system acquisition, and half thisbounded range (e.g. ±20 Hz) for a 1.5 T system acquisition. The highestpeak is located to determine it's width at the half-amplitude point.Next, the total spectral width of all parts of the spectrum which exceedthe half-amplitude point of the highest peak are determined. Theconfidence estimate is formed by taking the ratio of the spectral widthof the greatest peak divided by the total spectral width which exceedsthe threshold. If there is only a single peak above the threshold, theconfidence estimate will be 1.0, if there are many other peaks orspectral components which could be confused with the greatest one, thenthe estimate will approach 0.0. This provides a simple and robustestimate of the randomness or dispersal of energy in the vicinity of thewater peak. Like another approach using entropy measurement, e.g. asdescribed below, this current approach provides at least one desirablecharacteristic in that it's performance is substantially invariant withamplitude.

Yet another system and method to compute a confidence estimate that alsocan be appropriate is provided as follows. The spectral entropy iscomputed by normalizing the spectrum to take the form of a probabilitymass function. The Shannon entropy or uncertainty function, H, is thencomputed as follows:H=−Σp _(i) log₂ p _(i)where p=probability, and i=frequency index value (e.g. −40 to +40 hz).

It is to be appreciated that other approaches to quantify randomness oruncertainty of the spectrum may also be suitable for use with thevarious DDD_MRS signal processor aspects of the present disclosure.

For further understanding and clarity re: the ultimate impact frameediting as described herein, the unprocessed absorption spectrum plotfor all six channels from the patient (with the compromised framesincluded as aggregated in the respective channel spectra) in variousviews in prior Figures is shown for each respective channel in the sixindicated panes shown in FIG. 15 . The phase and frequency correctedspectrum averaged for selected channels 3 and 4, and for all 256acquired frames aggregated/averaged per channel, without applying frameediting and thus including the corrupted frames, is shown in FIG. 16A.In contrast, FIG. 16B shows a similar phase and frequency errorcorrected spectrum averaged for the same selected channels 3 and 4, butfor only 143 of the 256 acquired frames aggregated/averaged per channel(the remaining 113 frames edited out), per frame editing appliedaccording to the present embodiments prior to frequency errorcorrection. The peak value in the combined lactate-alanine (LAAL) regionof the frame edited spectrum of FIG. 16B is significantly increased—withcorresponding increase in SNR—relative to the peak value in the sameLAAL region of the non-frame edited spectrum in FIG. 16A (e.g. the peakvalue increases from about 3.75×10⁸ to about 4.4×10⁸), a nearly 20% SNRincrease despite about 40% corresponding reduction in the number of FIDframes used.

While the examples addressed above by reference to FIGS. 13-16B addressa highly beneficial embodiment for frame editing based upon watersignal, other frame editing embodiments are also contemplated, and manydifferent features of acquired DDD-MRS signals may be used for thispurpose. One such further embodiment is shown for example by referenceto another DDD-MRS pulse sequence acquisition for another disc inanother subject by reference to FIGS. 17A-F. More specifically, per thetime-intensity plot shown for this acquisition in FIG. 17A, while thewater signal region of the acquired spectral series (bright verticalband on left side of plot) reveals some shift artifact, another brightband appears at a broader region on the right side of the spectra,between about 150 and 200 FID frames into the exam. This region isassociated with lipid, and also overlaps with lactic acid (LA) andalanine (AL) regions of diagnostic interest according to the presentdetailed embodiments and Examples. This is further reflected in FIG. 17Bwhich shows a waterfall plot of running cumulative average of acquiredframes in series, where signal amplitude rises in this lipid-relatedspectral region during this portion of the exam. A resulting averagespectral plot for channels 1 and 2 of this acquisition, post phase andfrequency correction (again noting water signal did not prompt frameediting to remove many frames) is shown for reference in FIG. 17C. Thisresulting spectrum has significant signal peak intensity and line widthcommensurate with lipid signal, and which shrouds an ability to assessunderlying LA and/or AL chemicals overlapping therewith in theirrespective regions. Accordingly, an ability to measure LA and AL beingcompromised may also compromise an ability to make a diagnosticassessment of tissue based upon these chemicals (as if un compromised byoverlapping lipid). However, as this lipid contribution clearly onlyoccurs mid-scan, an ability to edit it out to assess signal without thatportion of the exam may provide a robust result for LA and AL-basedevaluation nonetheless.

This is shown in FIGS. 17D-F where only the first 150 frames of the sameacquisition are evaluated, which occur prior to the lipid contributionarising in the acquired signals. As is shown here, no lipid signal isrevealed in the time intensity plot of FIG. 17D, or waterfall plot ofFIG. 17E, or resulting final spectrum of FIG. 17F, though strongproteoglycan peak is shown with very little (if any) LA or AL in thesignal of otherwise high SNR (e.g. per the PG peak). As this exampleillustrates a DDD-MRS processed acquisition for a non-painful controldisc, strong PG signal and little to no LA and/or AL signal is typicallyexpected, and this thus represents a diagnostically useful, robustsignal for intended purpose (whereas the prior spectra without editingout the lipid frames may have erroneously biased the results).Accordingly, it is contemplated that a lipid editor may also be employedas a further embodiment for frame editing, with approaches forrecognizing lipid signal taken as elsewhere herein described (or as maybe otherwise available to one of ordinary skill and appropriatelyapplicable to this applied use).

Frequency Correction

As noted elsewhere herein, during the course of a typical single voxelDDD-MRS series acquisition cycle according to the pulse sequence aspectsof the present embodiments (e.g. about 2-4 minutes, depending uponsettings chosen for TR and number of frames), frequency errors can occurdue to patient motion and changes in magnetic susceptibility(respiration, cardiac cycle etc). In this environment where the acquiredspectral signals “shift” along the x-axis between multiple sequentialframes in an exam series, their subsequent averaging becomes“incoherent”—as they are mis-aligned, their averaging compromises signalquality. Unless this is corrected to “coherently” align the signalsprior to averaging, this error can result in an increase in line width,split spectral peaks and reduced peak amplitudes for diminished spectralresolution relative between signal peaks themselves (as well as reducedSNR). Accordingly, the DDD-MRS processor according to further aspects ofthis disclosure comprises a frequency error corrector that performsfrequency correction, such as for example prior to averaging frames, asalso represented schematically in the flow diagrams of FIGS. 9A-B.

This is performed according to one embodiment in the frequency domain.This is done by transforming the time domain data for each frame intofrequency domain absorption spectra, locating the water absorptionpeaks, and shifting the spectrum to align them to an assigned centerreference location or bin. Once shifted, the frame spectra are averagedin the frequency domain to generate the corrected or “coherent” channelspectra. In another embodiment, the desired frequency shift correctionfor a frame may be applied to the time domain data for that frame. Thetime domain data for all the frames would then be averaged with thefinal average then transformed back to spectra. While the processes arelinear and thus not dependent upon sequence of operation, it is believedin some circumstances that the latter embodiment may present slightlyincreased spectral resolution. In difficult signal acquisitionsituations, some of the frames do not have sufficient signal quality tosupport frequency correction. More specifically, water signal in someframes may be insufficiently robust to accurately “grab” its peak withhigh degree of confidence. This circumstance is addressed by anotheroperation of the DDD-MRS processor, frame editing in which the framesare omitted if the water peak cannot be identified with sufficientconfidence, also described herein (though may be performed independentof frame editing, which may not necessarily be required to be performed,despite the distinct benefits believed and observed to resulttherefrom).

The frame editing can be performed distinct from the frequencycorrection process (e.g., performed beforehand), or the frame editingand frequency correction can be performed simultaneously. The DDD-MRSprocessor can attempt to identify the water peak, calculate a level ofconfidence that the identified peak is water. If the confidence level isbelow a threshold, the frame can be disregarded. If the confidence levelis above a threshold, the water peak, as well as the rest of thespectrum, can be shifted to its proper alignment. The DDD-MRS processorcan then proceed to the next frame in the sequence.

Frequency error can be visualized using a time-intensity plot of theabsorption spectra of all the frames in an acquisition cycle. An exampleprocess and related results of frequency error correction according tothis present embodiment is shown and described by reference to FIGS.18A-21 for the same DDD-MRS series acquisition featured in FIGS. 7-8(prior to any correction) and FIGS. 11-12 (per prior DDD-MRS signalprocessing step of phase error correction). As shown in FIGS. 18A-19B(and similarly for prior FIGS. 13 and 17A), each acquisition frame isrepresented by a horizontal line, with amplitude of signal intensityacross the frequency spectrum indicated by brightness in grey scale(brighter shade/white designates higher amplitude, darker signalintensity indicates lower relative amplitude). The horizontal linesrepresenting individual acquisition frames are displayed in vertically“stacked” arrangement that follows their temporal sequence as acquired,e.g. time zero is in the upper left corner and frequency incrementedfrom left to right. The top 16 lines represent unsuppressed waterframes, with the remainder below representing suppressed wateracquisitions. The brightest portion of each line (left side of thetime-intensity plots) is reliably recognized as the water peakabsorption, typically the strongest signal of acquired MRS spectra inbody tissues.

Further to FIG. 18A, this plot for the original acquired sequence offrames from an acquisition series intended to be averaged is shownpre-frequency correction (e.g. with original frequency locations), andsimilar view but post-frequency correction is shown in FIG. 18B.Shifting of the location of this bright white water peak region, asobserved between vertically stacked frames, indicates frequency shift ofthe whole MRS spectrum between those frames—including thus the peaks ofspectral regions of interest related to chemicals providing markers forpain. The rhythmic quality observed in this frequency shifting, per thealternating right and left shifts seen around a center in theuncorrected plot (left side of figure) shift, remarkably approximatesfrequency of respiration—and thus is believed to representrespiration-induced magnetic susceptibility artifact. The contrastedplots seen in the pre and post frequency corrected time intensity plotsshown in FIGS. 18A-B reveal the process to achieve corrected “alignment”of the previously shifted signals for coherent averaging. For furtherclarity, each of two similar views of an enhanced contrast image (FIGS.19A-B)(though FIG. 19B reveals wider range of MRS Frames acquired in theseries), shows the original frequency shifted, incoherent mis-alignment(FIG. 19A) and frequency corrected, coherent alignment (FIG. 19B) of thewater peaks from this same acquisition series. In this example caseshown in FIGS. 18A-B and FIGS. 19A-B, all of the frames were ofsufficient quality to support frequency correction.

The frequency corrected absorption spectra for each acquisition cycleare averaged to generate an average frequency (and phase) correctedspectra for each channel, as is shown in FIG. 20 . The selected channels(channels 1 and 2) are then averaged to produce the final spectra (FIG.21 ) used for extraction of data along spectral regions of interest thatare considered relevant to DDD pain diagnosis. In comparing thephase+frequency error result of FIG. 21 against the phase errorcorrected-only result for the same acquisition series in FIG. 12 , asignificant increase in SNR and general signal quality is revealed inthe latter more fully processed case—showing for example an increasefrom slightly more than 11×10⁷ peak intensity with clear doublet in thePG region in FIG. 12 (where a doublet is not typically found, and likelyreflective result of incoherent averaging of the PG peak) to nearly18×10⁷ or an 80% peak intensity increase with narrower band and nodoublet in FIG. 21 , and also clearly higher PG/LA and/or PG/LAAL ratio,as are signal qualities elsewhere revealed herein to be of diagnosticrelevance in some highly beneficial applications. Still furthercomparison against the fully unprocessed spectral output from the MRscanner in FIG. 7 for the same acquisition series reveals even moredramatic signal quality, and in particular SNR, improvement.

The following documents are herein incorporate in their entirety byreference thereto:

-   1. Bottomley P A. Spatial localization in NMR spectroscopy in vivo.    Ann N Y Acad Sci 1987; 508:333-348.-   2. Brown T R, Kincaid B M, Ugurbil K. NMR chemical shift imaging in    three dimensions. Proc. Natl. Acad. Sci. USA 1982; 79:3523-3526.-   3. Frahm J, Bruhn H, Gyngell M L, Merboldt K D, Hanicke W, Sauter R.    Localized high-resolution proton NMR spectroscopy using stimulated    echoes: initial applications to human brain in vivo. Magn Reson Med    1989; 9:79-93.-   4. Star-Lack J, Nelson S J, Kurhanewicz J, Huang L R, Vigneron D B.    Improved water and lipid suppression for 3D PRESS CSI using RF band    selective inversion with gradient dephasing (BASING). Magn Reson Med    1997; 38:311-321.-   5. Cunningham C H, Vigneron D B, Chen A P, Xu D, Hurd R E, Sailasuta    N, Pauly J M. Design of symmetric-sweep spectral-spatial RF pulses    for spectral editing. Magn Reson Med 2004; 52:147-153.-   6. Pauly J, Le Roux P, Nishimura D, Macovski A. Parameter relations    for the Shinnar-Le Roux selective excitation pulse design algorithm    [NMR imaging]. IEEE Trans Med Imaging 1991; 10:53-65.-   7. F. Jiru, Europeant Journal of Radialogy 67, (2008) 202-217    The following U.S. Patent Application Publications are herein    incorporated in their entirety by reference thereto: US2008/0039710    to Majumdar et al.; and US2009/0030308 to Bradford et al.

DDD-MRS Diagnostic Processor and Use for Diagnosing DDD Pain

Development, application, and evaluation of a DDD-MRS diagnosticprocessor configured for use for diagnosing DDD pain based upon DDD-MRSacquisition series acquired from discs according to a DDD-MRS pulsesequence and DDD-MRS signal processor applications is disclosed byreference to the Examples and other disclosure provided elsewhereherein.

The diagnostic processing aspects of the present disclosure is alsorepresented schematically in the flow diagrams of FIGS. 9A and 9C, andgenerally includes multiple individual steps or operations: (1) regionalMRS spectral data extraction; and (2) diagnostic algorithm application.In addition, the diagnostic results will be typically displayed orotherwise produced in an appropriate fashion intended to satisfy anintended use. Furthermore, despite the many significant benefits of theDDD-MRS signal processor aspects herein disclosed for producing reliablyrobust MRS spectra from such DDD-MRS pulse sequence exams of discnuclei, certain results will nonetheless provide insufficient signalquality, such as due to low SNR below a threshold (e.g. 2 or 3), water“washout” of signal, lipid artifact, or obviously out of phase outervoxel artifact, for making reliable measurements in spectral regions ofdiagnostic interest (e.g. considered to represent certain chemicalbiomarker regions). In the event such poor quality signals were to enterthe diagnostic process of extracting data for diagnostic algorithmpurposes, the results would be much more likely to be corrupted by noiseartifact vs. real signal basis of the measured values, and couldpotentially yield diagnostically incorrect results.

Accordingly, the present disclosure according to further aspectsincludes a spectrum quality analyzer which determines which signalsotherwise passed through the DDD-MRS signal processor modules havesufficient signal quality to perform diagnostic algorithm, and which donot. As for the latter, these may be considered “indeterminate” orotherwise “failed test” results and thus not used diagnostically. Thismay prompt a repeat exam, perhaps with modified parameters intended tocounteract the underlying cause of such poor quality (e.g., low SNR orlipid artifacts), such as by re-voxelating according to a differentprescription (e.g., increasing voxel size, or decreasing voxel size, ormoving its location), adjusting water suppression, etc. In order toassist in appropriately directing such corrections in a re-exam, thespectrum quality analyzer may compare certain aspects of the subjectsignal against known features associated with such corruptions,determine the potential source of corruption, and flag and/or identifyto a user a suspected cause (and may further recommend one or morecourses of action to attempt correcting in a re-exam).

As this spectrum quality analyzer assesses the result of signalprocessing, it may be considered a part of the overall DDD-MRS signalprocessor. However, as it also comprises one of potentially multipleanalysis algorithms to determine “procedural failures” from theprocessed DDD-MRS acquisition and filter them out from furtherdiagnostic processing to an affirmative result, it may also in someregards be considered a portion of the diagnostic processor.

As still another embodiment of the diagnostic processor of the presentdisclosure, spectral data may be acquired for diagnostic purposes, suchas processing through a diagnostic algorithm, and thus a data extractoris also provided and as featured in FIGS. 9A and 9B. The data extractionor acquisition can typically involve recognizing regions along thespectrum generally associated with certain specific biomarker chemicalsof diagnostic interest (e.g. spectral regions of diagnostic interest or“SRDI”), and extracting target data from such SRDIs. These SRDI's willtypically have known ranges, with upper and lower bounds, along thex-axis of the spectrum, and thus making up “bins” that are defined forrespective data extraction. Examples of such bins are shown betweenadjacent vertical overlay lines in spectra shown in FIGS. 16A-B, 17C,and 17F (where top to bottom direction of a legend on the right of FIGS.17C and 17F corresponds with right to left direction of “bins” in thoseFIGS., though as also alternatively reflected with lead lines torespective chemical bin regions in FIGS. 16A-B). The typical SRDIs ofvarious biomarkers of interest are elsewhere described herein, and asmay be otherwise known in the literature and applicable for a givenapplication of the present aspects in practice. In some cases, it is tobe appreciate that such bins may provide only an ability to find acertain feature of the spectrum, e.g. a regional “peak”, and thisinformation can then be used to determine and extract other information(e.g. power under a peak region, which may be determined to includespectral power around the peak that extends outside of the respective“bin”). Furthermore, certain artifacts may cause chemical shift error inthe spectra despite corrections provided in the signal processing. Thisdata extractor may recognize a certain feature in one respective SRDIbin, e.g. PG peak, and then adjust the location for another target SRDIfrom where it might otherwise be sought (e.g. based upon a prescribeddistance from the first recognized target peak, vs. fixed relativelocations for the SRDIs along the x-axis). In some embodiments, tocompensate for slight shifts in the spectrum (e.g., chemical shifterrors) after a regional peak is identified in a specified bin, the binand/or the spectrum can be shifted to align the regional peak with thecenter of the bin, and an area under the curve can be taken for a region(e.g., in the shifted bin) centered on the located regional peak.

Once processed signal quality is confirmed, and spectral data extractionis performed, diagnostic processing based upon that extracted data maythen be performed, as also per schematic flow diagrams of FIGS. 9A-C.Such approaches are further developed below by way of the presentExamples, though it is to be appreciated that various different specificdiagnostic approaches, algorithms, uses, etc. may be performed by one ofordinary skill without departing from the other broad intended scopes ofthe current disclosure. Nonetheless, for purpose of understanding of thepresent detailed embodiments, the following bin region “limits” wereused for certain aspects of data extraction in the LA, AL, and PGregions of acquired and processed DDD-MRS spectra for general purpose ofmost data extracted and processed in the Examples: LA: 1.2 to 1.45; AL:1.45 to 1.6; PG: 2.0 to 2.2.

It will also be understood that the DDD-MRS diagnostic processor can beimplemented in a variety of manners, such as using computer hardware,software, or firmware, or some combination thereof. In some embodiments,the DDD-MRS diagnostic processor can include one or more computerprocessors configured to execute a software application ascomputer-executable code stored in a non-transitory computer-readablemedium. In some embodiments, the computer processor can be part of ageneral purpose computer. The computer processor(s) used by the DDD-MRSdiagnostic processor can be the same computer processor(s) used by theDDD-MRS signal processor, or it can be one or more separate computerprocessors. In some embodiments, the DDD-MRS diagnostic processor can beimplemented using specialized computer hardware such as integratedcircuits instead of computer software. The DDD-MRS signal processor mayalso be implemented by multiple computers connected, for example,through a network or the internet.

EXAMPLES Example 1

A DDD-MRS pulse sequence and signal processor were constructed toincorporate various aspects of the present embodiments disclosed hereinand were used and evaluated in clinical experience across a populationof discs in chronic, severe low back pain patients and asymptomaticcontrol volunteers. Various data extracted from features of interestalong the acquired and processed DDD-MRS acquisition series for discsevaluated in these subjects were compared against control diagnoses forsevere disc pain vs. absence severe disc pain, in order to develop andcharacterize a DDD-MRS diagnostic processor with the highest possiblecorrelation to the control diagnoses.

Methods:

Clinical Study Population: The study included 65 discs from 36 totalsubjects. Thirty-eight discs were from 17 patients with a clinicaldiagnosis of chronic, severe low back pain (LBP group), and 27 discswere from 19 asymptomatic volunteers (ASY Group). 25 discs in 12 of theLBP patients also received PD (PD Group) sufficiently contemporaneouswith the DDD-MRS exam to provide appropriate comparison basis. All 65discs were evaluated for single voxel magnetic resonance spectroscopypulse sequence and data acquisition (DDD-MRS), and signal processorparameter development of the new DDD-MRS approach. 52 discs from 31subjects were considered appropriate and used as controls for developingand assessing the DDD-MRS diagnostic processor for diagnosticapplication of the overall DDD-MRS system and approach. Thirteendiscography positive (PD+) discs from the PD Group were used as positivecontrol (PC) discs, and 12 discography negative (PD−) discs from the PDGroup plus all the ASY discs were used as negative control (NC) discs. Abreakdown summary analysis of demographics among and between thesegroups under this Example is shown in Table 2.

Study Design: Standard lumbar MRI was performed on all subjects. PDperformed within the PD Group was conducted by discographers per theirdiscretionary techniques, and in all cases was performed blinded toDDD-MRS exam information. However, the PD+ criteria included a painintensity score of greater than or equal to 6 concordant to typical backpain on PD; less than or equal to 50 psi above opening pressure (wheremeasured); and a negative control PD− disc in the same patient (exceptone). All PD-discs had less than 6 pain intensity scores per PD. Painquestionnaires, including Oswestry Disability Index (ODI) and VisualAnalog Scale (VAS), were completed by all subjects, and the PD Groupscored significantly higher than the ASY Group according to bothmeasures as shown in FIG. 22 (PD Group VAS and ODI on left side ofgraph, ASY Group VAS and ODI on right side of graph; VAS shown to left,ODI shown on right, within each group). The DDD-MRS pulse sequence andsignal processor constructed according to the various presentembodiments herein was used for each series acquisition for each disc,with data extracted from voxels prescribed at regions of interest withinnuclei of all discs included in the study. A 3.0 T GE Signa MRI systemand 8-channel local spine detector coil were used with the DDD-MRSpackage and approach (lower 6 of the 8 channels activated for lumbarsignal acquisition). Information along spectral regions of the acquiredDDD-MRS signals and associated with various chemicals of interest wereevaluated against control diagnoses across the PC and NC groups.

Multi-variate logistic regression analyses were performed to fit thedicotomous response (PC vs NC) to the continuous spectral measures anddevelop a binary DDD-MRS diagnostic set of criteria and threshold fordetermining positive (MRS+) and negative (MRS−) pain diagnoses. Areceiver operator characteristic (ROC) curve was generated, and areaunder the curve (AUC) was calculated to assess the accuracy of thedeveloped test (FIG. 23 ). Five-fold cross-validation was performed toassess the generalizability of the predictive relationship (FIG. 24 ).

DDD-MRS diagnostic outcomes for each disc were based on a single numbercalculated via the developed set of criteria based upon four weightedfactors derived from regions of the acquired MRS signals and associatedwith three chemicals—PG, LA, and alanine (AL). It is noted, however,that LA and AL regions are relatively narrow and immediately adjacent toeach other, and in some cases the true respective signals representingthese actual chemical constituents may overlap with each other and/orinto the adjacent region's location. Furthermore, either or both of theLA and AL regions may also overlap with possible lipid contribution,which was believed to be observed in some cases (which may includesignal from adjacent tissues such as bone marrow of bordering vertebralbody/s). Positive numerical threshold results were assigned “MRS+” asseverely painful, and negative results were assigned “MRS−” as notseverely painful. Accordingly, the threshold for severely painful vs.otherwise non-painful diagnostic result is zero (0). The set ofdiagnostic criteria used to determine MRS+ vs. MRS− diagnostic valuesaround this threshold with the most robust statistical correlation andfit to the control data observed across the disc population evaluatedfor this purpose is summarized as follows:Threshold=−[log(PG/LA*(0.6390061)+PG/AL*(1.45108778)+PG/vol*(1.34213514)+LA/vol*(−0.5945179)−2.8750366)];

-   -   wherein:    -   PG=peak measurement in PG region, AL=peak measurement in AL        region, LA=peak measurement in LA region, and vol=volume of        prescribed voxel in disc used for MRS data acquisition.

The distribution of DDD-MRS results according to these calculatedthresholds were compared against all PC and NC diagnoses, PD resultsalone, and portion of the NC group represented by the ASY group alone.Sensitivity, specificity, and positive (PPV) and negative (NPV)predictive values were also calculated per control comparisons.

Further aspects of the statistical methods herein applied, with respectto identifying diagnostic algorithm and also evaluating resulting data,are described in more detail below with respect to similar approachesalso taken in subsequent Examples 2 and 3.

Results:

DDD-MRS data demonstrated a strong correlation with the clinicaldiagnoses (R²=0.89, p<0.00001), with Receiver Operator Characteristic(ROC) analysis yielding an area under the curve (AUC) of 0.99 (FIG. 23 )and cross-validation through partition analysis resulting in onlydeminimus variance in the R² (FIG. 24 ). Tables 3 and 4, and FIGS.25A-27 , show various aspects of the resulting clinical comparison datafor DDD-MRS vs. control diagnostic data, which data and comparisons arefurther described as follows.

DDD-MRS results, with respect to binary MRS+ and MRS− diagnoses,correctly matched binary PC and NC diagnoses of painful/non-painful for50/52 (96.2%) discs evaluated across the PD and ASY groups. Of the 13MRS+ discs, 12 discs were from the PC group (PPV=92%). Of the 40 discsthat were MRS−, 39 were from the NC group (NPV=97%). DDD-MRS sensitivitywas about 92% and specificity was about 97%. Mean DDD-MRS results forthe PC and NC groups were 0.97±0.77 and −1.40±0.65 (R²=0.89, p<0.00001,FIG. 25B). As shown in FIG. 26 , DDD-MRS results matched PD results in23/25 (92.0%) discs of the PD Group: 12/13 (96.2%) of PD+ and 11/12(91.7%) of PD−. Mean DDD-MRS algorithm results for PD+ and PD− groupswere 0.97±0.77 and −1.39±0.72 (p<0.00001)(FIG. 25B). DDD-MRS resultsalso correlated with PD pain intensity scores (R²=0.73)(not shown).DDD-MRS results matched all 27/27 (100%) NC results represented by theASY group (FIG. 26 ). The mean DDD-MRS algorithm results for the ASYgroup were −1.4±0.63, which differed significantly vs. PD+(p<0.0001),but were not significantly distinguishable vs. PD-results (p=0.46)(FIGS. 25A-B).

As shown in FIGS. 28-29 , the DDD-MRS results according to this study ofthis Example provided highly favorable improvement vs. the diagnosticaccuracy typically attributed to MRI alone for diagnosing painful vs.non-painful DDD. More specifically, FIG. 28 (two bars on right side ofgraph) shows a comparison of the AUC for MRI alone vs. MRI+DDD-MRS, permeta analysis of previously reported AUC data for MRI for thisindication. This is further compared in the graph against a recent studyreporting AUC for MRI alone vs. MRI+PROSE for prostate cancer diagnosis(as compared to histopathological diagnosis of biopsy samples), where nosignificant improvement was shown by the additional inclusion of PROSEapplication of MRS within the MR-based diagnostic regimen. While theprostate data reflected within the graph reflects a larger relativepopulation of samples in multi-center study, and the DDD-MRS paindiagnostic results shown reflects a smaller population within singlecenter experience, the dramatic relative improvement presented by theDDD-MRS approach in the single center experience is expected to carryover to a significant degree into larger, multi-center context for thisapplication. Further to FIG. 29 , the results of this study additionallyshow improvement to positive and negative predictive values by enhancingstandard MRI alone with the addition of the DDD-MRS diagnostic—per metaanalysis of the current data vs. previously published data for MRI forthis purpose.

While the other information described herein is clearly sufficient todemonstrate the remarkable utility of the present embodiments inoperation for the indicated purpose of this Example, further supportiveinformation is also provided as follows. The DDD-MRS diagnostic exam wasalso evaluated for and demonstrated robust repeatability, as reflectedin FIG. 30A. More specifically, FIG. 30A shows DDD-MRS diagnosticalgorithmic results according to this Example for eight (8) L4-L5 discsin eight (8) asymptomatic pain free volunteers examined twice—each on 2separate dates, with trend between sequential results for each discshown in respective lines between columns (1) and (2) along the x-axisof the graph. These were all negative diagnostic results, indicatingpain free diagnosis according to the exams, with relative repeatabilityand little variance between exams on average between the group andindividually for the vast majority of the samples (with one obviousoutlier demonstrating more variance than the others, though stillnonetheless representing a repeatable diagnostic result as negative). Inaddition, as shown in FIG. 30B, the measured ratios between metaboliteregions for PG and a combination of LA and AL (alanine) or “LAAL” werecompared as per spectral acquisitions and extracted regional datameasurements in vivo, against measurements taken for the same chemicalregions but via 11T HR-MAS spectroscopy ex vivo after surgical removalfor pain treatment. These comparisons were highly correlative, withR²=0.98, demonstrating the robustness of the measurements taken in vivoby ex vivo validation measurements for the same disc material.

Certain benefits provided by the DDD-MRS processor for post-processingacquired MRS signals were also evaluated across a sub-set sampling ofthe DDD-MRS data derived from the clinical population under this studyof this Example. In particular, for each series acquisition the SNR ofthe processed DDD-MRS signals (“DDD-MRS spectra/spectrum”) wascharacterized, and compared against the 6 channel average, non-phase orfrequency corrected, GE Signa output spectra as acquired“pre-processing” according to the present embodiments (e.g. “inputcombined spectra/spectrum”). This SNR characterization and comparisonexercise was conducted as follows.

A freeware digitization program (WinDIG™, Ver 2.5, copyright 1996, D.Lovy)) was used to digitize both final DDD-MRS results and “screen shot”images. The “screen shot” images were reverse-imaged using MS Paintprior to digitization. The output of the digitizer program is an arrayof integers in a comma-separated values (CSV) file format. The CSV datafiles were imported to Microsoft™ Excel™ and re-plotted as shown inFIGS. 31A-B. A “region of interest” on the chemical shift (CS) axis(x-axis) pertaining to metabolite proteoglycan (PG, CS=2.11 PPM) wasdeemed to be the “signal”. A region of interest to the far right (CS=0.5PPM) which would not typically contain any spectral activity was deemedto be the “noise”. In the event there was not a significant spectralpeak in the PG region which is the often the case on pain patient discs,then the lactate/Lipid region of interest (CS=1.33 PP) was used as thesignal. The “ranges of interest” were visually determined on both imagesresulting in sections of the data array. The SNR of a waveform isexpressed as:10*log₁₀(RMS signal/RMS noise).

The RMS value was calculated by taking the sum of squares of the datasection, calculating the mean of the sum of squares, and then taking thesquare root of the mean. Since the spectra are power amplitude plots,the log base 10 of the ratio of the RMS values is then multiplied by 10to generate the SNR in dB.

For further understanding of this approach and examples of the digitizedspectra and information extracted therefrom, FIG. 31A shows a digitizedDDD-MRS spectral plot and accompanying SNR information, whereas FIG. 31Bshows similar views for a digitized pre-processed all channel (n=6)averaged output spectral plot output from the respective MR system andrelated SNR information for the same acquisition series (withoutprocessing according to the present signal processing aspects of thepresent disclosure).

These pre- and post-processing SNR results are shown in FIGS. 31C-F.More specifically, FIG. 31C shows the calculated SNR for the pre- andpost-processed spectra, with significant majority of the pre-processedspectral SNR shown on the left side histogram distribution of the plotfalling below 5 (and also much of the data below 3), but with asignificant majority of the post-processed spectral SNR shown on theright side histogram distribution of the plot falling above 3 (allbut 1) and even above 5 (all but just 2). A typical accepted SNR rangefor confidently measuring chemical constituents from an MRS plot is inmany cases over 5, though in many cases may be for any data over 3—suchthat below these thresholds may be “unquantifiable” or “immeasurable” atleast per such standards (if applied). In such an application of thesethresholds, it is clear that a significant portion of data acquiredpre-processing according to the present embodiments is not generallyuseful for interpretting signal regions of interest, whereas these dataas post-processed herein become quite consistently useful. In fact, asshown in FIG. 31D, the average SNR across the signals evaluated for thiscomparison exercise was: about 3 (e.g. well below 5) pre-processing, andabout 13 (e.g. well above 5) post-processing (p<0.001). As per the ratioof post- vs. pre-processed signals further shown in FIG. 31E, in allcases compared the post-processed signals were higher SNR thanpre-processing, generally along a range between 2 to 8 times higher SNR(with only one point falling below 2× improvement, though still about50% improved). As further evaluated (e.g. FIGS. 31F-H), the meanabsolute improvement was about 10 dB, the mean ratio improvement wasover 4×, and the mean % improvement was well over 300% in convertingfrom pre- to post-processed signals according to the presentembodiments.

For further illustration of the beneficial results demonstrated by theDDD-MRS diagnostic exam, FIGS. 32A and 32B show two different examplesof DDD-MRS diagnostic display results for two different patients in theclinical study featured under this Example 1. These patients havesimilar disc degeneration profiles as seen on the MRI images, with darkdisc at L5-S1 and relatively healthy discs revealed above at L4-L5 andL3-L4 in each patient. As also shown in each of these figures, bothpatients also had positive discogram results at L5-S1. However, as alsoshown in these two comparison Figures, the patient featured in FIG. 32Ahad a negative discogram result (e.g. non-painful diagnosis) at L4-L5,whereas the patient featured in FIG. 32B had a positive discogram result(e.g. painful diagnosis) at that level—despite having similar discdegeneration profile. As a consequence of both exams, with moderndiscography technique guidelines indicating requirement for a negativecontrol disc before positive levels may be accepted results, thepatients each had another negative discogram done at the L3-L4 (FIG.32A) and L4-L5 (FIG. 32B) levels, respectively, to provide the requirednegative control level. As an awarded recent study has shown discographysignificantly increases disc degeneration and herniations rates, theresult of both of these studies, if followed for directed intervention,would have resulted in treating the positive discogram levels, but notthe negative discogram levels—leaving those untreated levels in place topotentially accelerate in degeneration and toward possible herniations.As shown in these Figures, the non-invasive DDD-MRS results matchedthese invasive discography results at all disc levels. The DDD-MRSapproach provides the distinct benefit of providing the diagnosticinformation required, while leaving all discs uncompromised due to thenon-invasive nature of the approach.

Discussion:

The differentiation of painful and non-painful lumbar degenerative discsis an important goal in the accurate assessment of pain generators, andin guiding clinical management of patients with lumbar degenerative discdisease. The novel application of Magnetic Resonance Spectroscopydeveloped and evaluated under this study proposes a non-invasive,objective, and quantifiable measure of the chemical composition of thelumbar intervertebral disc. The MRS diagnostic algorithm developed andused in this study demonstrates a high degree of sensitivity inidentifying patients with a clinical assessment of lumbar discogenicpain and a positive discogram, and a high degree of specificity inidentifying levels that are not painful, without any false positiveresults observed in asymptomatics. This study developing, uniformlyapplying, and characterizing the DDD-MRS diagnostic approachretrospectively across the study population evaluated herein is quiteencouraging. Cross validation also performed on the results predicts theapproach is generalizable to broader population, as may be readilyconfirmed in additional prospective study in more subjects, and as maybe conducted by one of ordinary skill.

Example 2

The 52 disc clinical data set evaluated under the DDD-MRS systemembodiments of the present disclosure and associated with Example 1 wasfurther expanded with additional new subjects examined for a total of 74discs, with additional signal processing developments performed anddiagnostic algorithm development conducted to determine the optimalcorrelation to the expanded data set. The results of this algorithmdevelopment and analysis was then applied to an additional 5 discs innew asymptomatic control volunteers prospectively, for 79 total discslater evaluated.

Standard logistic regression procedures were used to develop a secondgeneration linear regression model between disc variables obtained fromDDD-MRS acquisitions and processed by the DDD-MRS signal processingengine, to disc pain status (pain/no-pain entered as a categoricalvariable based on provocative discography). MR spectra (in-phase realpower format) from a population of 74 discs (15 painful and 59asymptomatic) were used for classifier development and cross-validationpartition analysis. The DDD-MRS data demonstrated a strong correlationwith the clinical diagnoses (R²=0.76, p<0.00001) with an ROC analysisyielding an AUC of 0.99. Cross-validation through partition analysisresulted in only small variance in R².

Materials and Methods

All statistical analyses were performed using JMP (version 7.0, SAS).Standard logistic regression procedures were used to relate the discvariables (proteoglycan, lactate, and alanine spectral peaks entered ascontinuous variables) to the disc pain status (pain/no-pain entered as acategorical variable). Discography performed according to ISISGuidelines was used as the reference standard for pain status in lowback pain patients. Discs from asymptomatic volunteers were assumednegative. The discography status and disc variables were entered into anexcel spreadsheet and imported into JMP.

The DDD-MRS diagnostic algorithm was determined in a three-stageprocess.

First, the terms were limited to spectral features related toproteoglycan, lactate and alanine because these were shown to beimportant classifiers in prior studies (Keshari, 2008. “Lactic acid andproteoglycans as metabolic markers for discogenic back pain.” Spine33(3): 312-317), and fit with biologically-plausible theories fordiscogenic pain generation. In addition, normalized values for thesefactors were considered. To provide an estimate of metaboliteconcentration, the spectral measures were divided by the region ofinterest (ROI) volume. Also, given signal strength may vary with ROIdepth, subject body mass index (BMI) was also considered as anormalizing factor. This was done by taking the BMI for a subjectassociated with a given disc sample being evaluated divided by theaverage BMI across the data set used for the logistic regressionmodeling. Also as raw signal region values represent “amounts” ofrespective chemicals associated such regions, dividing such values byvoxel volume may provide a surrogate approach to more closelyapproximating “concentration” for such chemicals (amount/unitvolume)—which as biomarkers as mediators to a pain cascade are likelymore relevantly assessed as concentration. For example, lactic acid ismore relevant to disc tissue acidity, which is believed to be a paingenerator, on a concentration basis vs. total amount in the tissue.Accordingly, voxel volume adjustment for a signal measurement simplyinvolved dividing the measured factor or parameter by the voxel volume.

In the second step, the form of the factor dependence was estimatedusing Screening Platform in JMP. Within the Screening Platform, thedependent variable was chosen to be pain status, and the candidateindependent variables were chosen to be proteoglycan, lactate, andalanine (either raw values or values normalized by voxel volume and/orBMI). The Screening Platform then identified candidate terms withassociated p-values. These would include either individual factors, orproducts of multiple factors. Terms with p-values less than 0.05 wereselected as candidates for further consideration.

In the third step, candidate terms from the Screening Platform wereentered as independent predictors in the Logistic Platform of JMP. Thisplatform was used to conduct logistic regression analysis to identifystatistically-significant terms plus their parameter estimates. TheLogistic Platform fits the probabilities for the response category(pain/no-pain status) to a set of continuous predictors (metaboliteterms). The fit quality was judged by the coefficient of determinationR² and the p-value. In an ad-hoc stepwise fashion, candidate terms werebrought into the Logistic model to judge their influence on modelperformance.

Because some metabolite data are not normally distributed, log andsquare-root transformations of the candidate terms were also considered.Candidate terms with p-values less than 0.05 were removed from themodel. The Logistic regression output provided parameters that aremultipliers for each term plus an intercept term. These formed analgorithm that provides a continuous number that, if greater than zerowould indicate a painful status, and if less than zero would indicate anon-painful status.

As an additional summary of the discriminatory accuracy of the Nociscandiagnostic algorithm, generated standard Receiver-Operator curves (ROC)that are plots of sensitivity versus specificity across a rank orderedlist of study discs. The area under the ROC curve (AUC) was used tojudge the algorithm accuracy. The AUC is the probability that testresults for a randomly-selected painful disc and non-painful disc willbe rank ordered correctly. Additionally, continuous correlationprocedures were used to judge whether the output of the diagnosticalgorithm correlates with VAS score, disc degeneration grade, and thediscography pain intensity.

Results/Data

Using the aforementioned procedures, a diagnostic algorithm wasdeveloped using a 74 disc (15 pain, 59 control) population. The best-fitlinear regression equation result using this approach was as follows:Score=−4.6010405+1.58785166(BLA)−0.081991(VBLAAL−29.3125)*(VBLAAL−29.3125)+0.01483355(PG/MAXLAAL−7.14499)*(PG/MAXLAAL−7.14499)*(PG/MAXLAAL−7.14499)+0.1442603(MAXLAAL/vol−16.1202)*(VBLAAL−29.3125)−0.0008879(VBLAAL−29.3125)²*(MAXLAAL/VOL−16.1202)where BLA is the BMI corrected LA spectral peak, VBLAAL is the ROIvolume and BMI normalized sum of the LA and AL spectral peaks, MAXLAALis the maximum of either the LA or AL peaks, and PG is the n-acetylspectral peak.

The present linear regression equation of this Example 2 uses similarfeatures as its predecessor such as chemical peak values and peakratios, but in addition uses features normalized for voxel volume andBMI (e.g. “VB” designating both). Increased body fat (increased BMI)will reduce chemical peak values because the voxel is physically furtheraway from the RF coil resulting in reduced signal strength and chemicalpeak values. The BMI value is adjusted (normalized) by the mean BMI ofthe population. The adjusted BMI value thus applies a proportional“gain” to chemical peak values otherwise reduced by large BMI.

Similarly small voxel volumes will reduce the chemical peak values andthe inverse of voxel volume is applied as a “gain” factor. In additionto normalization, the equation also defines a two new features. Thefirst consists of combined regions of interest (ROI) lactate (LA) andalanine (AL) regions to create LAAL. The second is a region calledMAXLAAL whose value is the greater of the two regions.

A final development to the diagnostic engine is the application of anindeterminate band to the classification process. This band lies betweenthe highly probable pain and pain free states and is statisticallydetermined from the distribution of the two disc populations. Diagnosticscores that fall within this band are determined to be proceduralfailures because of the low probability to diagnose either way. Whenapplied this band results in one false negative (a positive discographydisc diagnosed as pain free).

Results and Discussion

A second generation diagnostic classifier using DDD_MRS acquisition dataas processed by the Nociscan signal processing average has beendeveloped using an increased disc population (from n=52 to n=74). Theincorporation of BMI adjustment per each sample's BMI relationship to amean population BMI, voxel volume adjustment to more closely approximateconcentration aspects of the target biomarker metabolites, and the useof combined regions of interest (LAAL, MAXLAAL), has resulted in alinear regression equation with a significant improvement over theotherwise highly accurate first generation linear regression equation,with (R²=0.89, p<0.00001) with an ROC analysis yielding an AUC of 0.99.

For further illustration, FIG. 34A shows the distribution of thisalgorithmic data across the combined data set via the formulaicalgorithmic “score” for each disc plotted against designation of thediscs as fitting within the positive controls (positive discogrammeddiscs, or PD+), negative controls (negative discogrammed discs in painpatients, or PD−, combined with discs from asymptomatic controlvolunteers or “ASY” discs), and versus these two negative controlsub-populations alone. This also shows the application of thestatistically guided “indeterminate” band bordering the “zero” line andwhere n=5 discs fall, with positive test results above the upper limitof that band (n=12), and negative results below the lower limit of thatband (n=63, of which 62 were negative controls and 1 was a positivecontrol disc). Excluding these indeterminates as “procedural failures”excludes 5/79 discs or only 6% of the test population, while remaining94% are considered procedural successes for making a confidentdiagnosis. Among these 94% procedural successes, the results were 99%accurate with 73/74 overall match to controls (only 1 mismatch),R²=0.91, p<0.0001, and AUC=0.99. These more detailed breakdown for thematches against controls (e.g. positive match to positive discogram, ornegative match to negative discogram or discs from asymptomaticsubjects) are as follows: 12/13 (92%) of Positive Discography; 13/13(100%) of Negative Discography; 48/48 (100%) of Asymptomatics—thus therewere no false positive results in 62 negative combined controls, andonly 1/13 presumed false negative result among 13 positive controldiscs. These results further provide the following performancecharacteristics typically used to evaluate a diagnostic platform: 92%Sensitivity, 100% Specificity, 100% Positive Predictive Value, and 98%Negative Predictive Value.

For still further illustration of another highly beneficial view ofthese highly accurate results of this current approach of Example 2 tothis test group, FIG. 34B shows another distribution of the same resultsfor this same data set, but as converted to % probability prediction adisc is painful (as generated by rank ordering of the logisticregression analysis and results). As shown in this Figure, a regionbetween about 80% probability and about 20% probability prediction ofpain corresponds with capturing the same n=5 discs indeterminate zonediscs of the other view of the data distribution in the prior Figure,with greater than about 80% probability criteria capturing all of then=12 same positive test results (all matching positive controls), andless than about 20% probability criteria capturing all of the n=63 samenegative test results (62 matching all of the negative controls, and 1 apositive control and thus representing the same single presumed falsenegative test result).

Example 3

Standard logistic regression procedures were used to relate discvariables obtained from DDD-MRS acquisitions and processed by theNociscan signal processing engine to disc pain status (pain/no-painentered as a categorical variable based on provocative discography).Acquired DDD-MRS spectra were processed, analyzed, and presentedpost-processing for diagnostic purposes in absorption mode—vs. real-partsquared power format of prior Examples. The spectral acquisitions werethe same and from the same population of 79 discs in 42 subjects (15painful and 64 asymptomatic) as featured in Example 2, used here forfurther algorithmic classifier development. Certain signal qualitycriteria were also used in this Example 3 to determine each of threeclassifications of acquired results—namely recognizing the followingsub-groups: (1) a first spectral group with clearly apparent lipidsignal (then given its own logistic regression model and resultingalgorithm), and (2) a second spectral group absent any obvious lipidsignal that was still further sub-classified into still furthersub-groups: (2)(a) spectra with significant PG/LAAL peak ratios over adetermined criteria threshold, and (2)(b) the remaining non-lipidsignals not meeting this criteria also given its own second logisticregression model and resulting algorithm. The three classifier equationsthat were developed resulted in 100% procedural success and 100%separation for differentiating painful from non-painful discs in all 79discs evaluated.

Purpose

The purpose of this study was to evaluate still further potentiallyvaluable approaches for developing a robust classifier, including asusing features extracted from absorption spectra as opposed to featuresformerly extracted from in phase real power spectra, and also toevaluate a different approach for classifier modeling based upon aserial application of a limited few tests applied to what appeared to beunique sub-populations among data. Absorption power format is thetraditional method of displaying spectra. In phase real power spectraare comprised of the square of the real component of each spectralpoint. This format presents only positive going spectra with minimalbaseline shift. This mitigates the need to fit a spline curve to thebaseline as well as makes the spectra appear more peaked. The overalleffect is to enhance the apparent signal to noise ratio (SNR) and removethe variability associated with fitting a baseline to the spectral plotfor the purpose of making spectral peak and area under the curve (AUC)measurements. Nonetheless, the current absorption spectra approach ofthis Example 3 is more common to typical MRS analysis in otherapplications, and may be more relevant for biomarker assessment incertain cases, vs. previous classifier development of prior Examplesthat has been done using spectra presented in in-phase, real powersquared format.

Materials and Methods

A comparison of SNR for post-processed versus pre-processed DDD-MRSspectra acquired per this Example was performed similarly as featuredabove for Example 1 data set (e.g. FIGS. 31A-H), except using absorptionspectra for both pre- and post-processed data, and per the expandedclinical data set represented in this Example 3. These were otherwiseanalyzed similarly as was done in those prior Figures for the priorExample 1.

All statistical analyses were performed using JMP (version 7.0, SAS).Standard logistic regression procedures were used to relate the discvariables (proteoglycan, lactate, and alanine spectral peaks entered ascontinuous variables) to the disc pain status (pain/no-pain entered as acategorical variable). A significant majority of the discography wasperformed according to ISIS Guidelines and was used as the referencestandard for pain status of ‘positive control’ discs in low back painpatients. Discs from asymptomatic volunteers were assumed negative, andwere combined with negative discography discs from the pain patients asthe negative control group presumed to be non-painful. The discographystatus and disc variables were entered into an excel spreadsheet andimported into JMP.

The terms in each of the two sub-groups (1) and (2)(b) where logisticmodeling was applied for algorithm development were determined in athree-stage process. The first step choosing spectral features ofinterest for analysis, and corresponding to the PG, LA, and AL biomarkerchemicals, proceeded as per prior examples, and including BMI and voxeladjustment as described for Example 2, with the following difference inthis Example 3 that absorption spectra were used for the data extractionand subsequent inputs into the diagnostic processor.

In the second step, the form of the factor dependence was estimatedusing Screening Platform in JMP. Within the Screening Platform, thedependent variable was chosen to be pain status, and the candidateindependent variables were chosen to be proteoglycan, lactate, andalanine (either raw values or values normalized by ROI volume and/orBMI). The Screening Platform then identified candidate terms withassociated p-values. These would include either individual factors, orproducts of multiple factors. Terms with p-values less than 0.05 wereselected as candidates for further consideration.

In the third step, candidate terms from the Screening Platform wereentered as independent predictors in the Logistic Platform of JMP. Thisplatform was used to conduct logistic regression analysis to identifystatistically-significant terms plus their parameter estimates. TheLogistic Platform fits the probabilities for the response category(pain/no-pain status) to a set of continuous predictors (metaboliteterms). The fit quality was judged by the coefficient of determinationR² and the p-value. In an ad-hoc stepwise fashion, candidate terms werebrought into the Logistic model to judge their influence on modelperformance.

Because some metabolite data are not normally distributed, log andsquare-root transformations of the candidate terms were also considered.Candidate terms with p-values less than 0.05 were removed from themodel. The Logistic regression output provided parameters that aremultipliers for each term plus an intercept term. These formed analgorithm that provides a continuous number that, if greater than zerowould indicate a painful status, and if less than zero would indicate anon-painful status.

As an additional summary of the discriminatory accuracy of the Nociscandiagnostic algorithm, generated standard Receiver-Operator curves (ROC)that are plots of sensitivity versus specificity across a rank orderedlist of study discs. The area under the ROC curve (AUC) was used tojudge the algorithm accuracy. The AUC is the probability that testresults for a randomly-selected painful disc and non-painful disc willbe rank ordered correctly. Additionally, we used continuous correlationprocedures to judge whether the output of the diagnostic algorithmcorrelates with VAS score, disc degeneration grade, and the discographypain intensity.

In context of the aforementioned methods and procedures applied toprevious classifier development and those receiving logistic regressionmodeling in this current Example, a data partition approach wasimplemented based on certain spectral features observed in the currentdataset. First, discs with perceived lipid signal in the acquiredDDD-MRS spectra were partitioned into Group A (n=10). This was given itsown logistic regression modeling as test #1. Next, because many negativenon-painful discs were observed to have uniquely strong n-acetyl peak(PG) and weak lactate (LA) and/or alanine (AL) peaks, the PG/LAAL ratiosfor the remaining non-lipid disc population (n=68) were evaluatedbetween the positive and negative control groups. A cut-off in ago/no-go voting method approach of test #2 for ‘clearly negative’ discswas identified at PG/LAAL peak ratios above 1.81 to create Group Bsuccesses for negative results as non-painful (n=52 of 68 discsevaluated in the non-lipid population). The third data analysis and testportion, Group C (n=16, a subset of non-lipid Group B that did not meetthe test #2 criteria as having PG/MAXLAAL<1.85) were analyzed also usingthe logistic regression modeling per the three-step process definedabove. Four statistically-significant terms and their parameterestimates were identified by the Logistic Regression Platform: ROI (e.g.voxel volume or VV) and BMI adjusted LA absorption peak; VV and BMIadjusted AL absorption peak; VV and BMI adjusted AL AUC (area under thecurve) or “ALAUC”; and square root of the VV and BMI adjusted N-acetylAUC or “NAAAUC”).

Finally with respect to the DDD-MRS diagnostic processor aspects of thepresent Example, the spectra with suspected lipid contamination (GroupA) were also analyzed using the three-step analysis procedure. Thisresulted in two terms that separated positive from negative discs: thesquare root of the VV and BMI adjusted LA peak, and the VV and BMIadjusted ratio of n-acetyl to LAAL. When taken together, the partitionplus logistic regression approach success fully separated all negativefrom all positive discs.

Results/Data for Absorption Spectra SNR

The SNR evaluation of the post-processed versus pre-processed absorptionspectra plots per this Example are shown in FIGS. 34A-F, and demonstratesignificant SNR increase via the DDD-MRS signal processor aspectsdeployed for this data set in the Example 3, and also shows vastmajority of the resulting signals to have sufficiently robust SNR fortarget regional chemical signal feature measurements. More specifically,FIG. 34A shows vast majority of the post-processed SNR above 3 (exceptonly 2 cases), and in fact over 5, though much of the pre-processedspectra were below these levels. FIG. 34B shows the averagepre-processed SNR of only slightly above 4, while the averagepost-processed SNR was about 8 and nearly ⅔ of the post-processed SNR ofthe real-part squared approach taken in the prior Example despite thatapproach squaring the signal:noise values. FIG. 34C shows the vastmajority of the individual points were improved (e.g. ratio of SNR ofpost- vs. pre-processed signals), but for only a few (n=3) which werefurther observed to be quite high SNR to begin with, and with FIG. 34Dshowing about a 3.5 dB average SNR increase or about 2.2× (FIG. 34E)versus the pre-processed SNR.

As for the DDD-MRS diagnostic processor developed and evaluated per thisExample, the best fit linear regression equations extracted from theabsorption spectra are shown as follows:

Group A, test #1:Score=−(−335.51971+0.00010632*(LAVVBMI)²+873.744714*(PG/(LAALVVBMI)));where LAVVBMI equals the voxel volume and BMI adjusted LA peak value.

Group B, test #2:Score=−(−1.4959544+1.72223147*(PG(MAXLAAL)));where PG/MAXLAAL equals the PG peak value divided by the maximum peakvalue of the LAAL region.

Group C, test #3:Score=−1*(−134.40909800961+3.96992556918043*LAVVBMI−2.6198628365642*ALVVBMI+113.683315467568*ALAUCVVBMI−149.65896624348*SQRT(PGAUCVVBMI));where LAVVBMI is the voxel volume and BMI adjusted LA peak value,ALVVBMI is the voxel volume and BMI adjusted AL peak value, ALAUCVVBMIis the AL region area under the curve as voxel volume and BMI adjusted,and PGAUCVVBMI is the PG region area under the curve as voxel volume andBMI adjusted.

Results/Data and Discussion—Diagnostic Processor

The default model used by JMP is to distribute data around 0. Resultswill typically provide negative results above 0, and positive resultsbelow 0. However, as this is inverse to logical presentation to matchthe classifications, and as in prior Examples, the negative of theclassifier outputs are taken so that positive scores are associated withpositive clinical tests for pain and negative scores are associated withnon-painful discs.

The partitioning of spectral acquisitions based on the presence of lipidsignal and on “clearly non-painful” spectral attributes(PG/MAXLAAL>1.81), as taken from absorption spectra, distinguish thisclassifier approach from previous efforts. The logistic regressionmodeling of the resulting sub-groups also provide different algorithmsand varied specific factors as a result. Group A contains spectra withsufficient lipid (lipid peak at 1.3 PPM) that prevents the discretecharacterization other chemical components such as PG, LA and AL. It isnoted that upon evaluation of the absorption spectra results for thisexample, one acquisition or n=1 of n=79 total overall discs initially tobe evaluated, was not considered to have sufficient signal quality (e.g.SNR too low) for robust diagnostic processing and thus excluded fromthat stage of processing, with resulting population of n=78 evaluateddiagnostically of n=79 attempted (e.g. 99% procedural success, and <1%procedural failure rate due to low SNR processed acquisition).

An example of a Group A spectra including suspected lipid signal from anasymptomatic control L5-S1 disc is shown in FIG. 35 . Source of lipid ina given signal is not known, and may come from several differentsources. Lipid signal is often observed however to result from capturinglipid-enriched vertebral body endplates by the voxel prescribed, andoften (though not always) in an oblique, severely compromised (crushed)L51 disc. Another source of lipid contamination may be due to patientmovement during the MRS acquisition, also involving end-plate artifact.It may also come as out of voxel signal in some cases, and may in factcome appropriately from within discs. Nevertheless, the prior groupingof signals with and without lipid was successful in accuratelydiagnosing most all discs, including all spectra with lipid across allthe Examples. In this Example 3, spectra of Group A (n=10) was separated(100%) into painful and non-painful groups per test #1 and an associatedprobability of being painful is shown in FIG. 36 . It is also observedamong these spectra in this Group A that the presence of a sufficientlystrong PG component in combination with lipid signal is likely relatedto sufficiently correlating with non-painful discs to provide theresulting reliable differentiation between positive and negative controlgroups.

The second partition was applied to the disc population without lipidcontamination (n=68). By visual observation of spectra across thispopulation it was noted that all discs with PG/MAXLAAL value exceedingabout 2. In further analysis, a threshold value of 1.85 was identifiedto partition only non-painful negative control discs above thethreshold, and completely isolating the painful positive control discpopulation below the threshold, but while also including othernon-painful negative control discs below this threshold. This PG/MAXLAALpartition analysis is shown in FIG. 37A. The more statistically robustlinear regression model of test #2 derived and applied to Group B (n=68)is shown in FIG. 37B. The painful vs. non-painful segregations remainsimilar to the immediately previous analysis. The % probability painfulconverted format of this data distribution is shown in FIG. 37C, withthreshold nearly approaching 20% differentiating all the same negativecontrol discs, and none of the positive control discs in the group,below. (Note: Probability of being non-painful=1−pain probability).

The sub-population of discs from Group B with a PG/MAXLAAL<1.85 arepartitioned into the third Group C (n=16), with the linear regressiontest #3 derived from Group C resulting in the data distribution shown inFIG. 38 . There is 100% separation between these remaining positive andnegative control discs in this final Group C.

The ultimate result of this applied step-wise partitioning and logisticregression diagnostic algorithm approach was 100% separation betweenknown painful vs. non-painful results, across all of the 78 discsevaluated diagnostically.

Nonetheless, it is to be appreciated that other specific diagnosticalgorithmic approaches may be applied and also achieve significantlyrobust results. As one example, a hyrbrid linear regression equationconsisting of terms from test #2 and test #3 (derived from Groups B andC respectfully) is provided by algorithm test #4 for Group B, shownpartitioned in FIG. 39 . This approach as evaluated here also stillretains all 78 test discs in the overall population while resulting in76/78 overall match to controls per only 2 presumed false negativevalues (two PD+ discs indicated instead as negative DDD-MRS tests asbeing pain free), and no false positive results. The hybrid linearregression equation coefficients range within two orders of magnitude ofeach other and are fully normalized or proportional, characteristicsthat make for a robust classifier. The hybrid equation mitigates theneed to perform the PG/MAXLAAL partition.

Group B, Test #4:Score=−6.94869+0.05035*LAVVBMI−0.028534*ALVVBMI−0.51761*SQRT(PGAUCVVBMI)+0.36976*ALAUCVVBMI+4.04875*PG/MAXLAAL;where LAVVBMI=LA peak adjusted by voxel volume and BMI, ALVVBMI=LA peakadjusted by voxel volume and BMI, PGAUCVVBMI is the PG area under thecurve (AUC) adjusted by voxel volume and BMI, ALAUCVVBMI is the AL areaunder the curve (AUC) adjusted by voxel volume and BMI, and PG/MAXLAALis the ratio PG peak to the maximum peak of either LA or AL.

According to the Examples 1-3 evaluating DDD-MRS diagnostic processoraspects of the present disclosure across clinical experience and data,features from in phase power and absorption spectra may be used todevelop diagnostic classifiers with a high correlation to standardcontrol measures for differentiating painful from non-painful discs,including highly invasive, painful, costly, and controversialneedle-based provocative discography. The Example 3 in particular,pursued according to the present DDD-MRS embodiments of this disclosure,demonstrate that data from absorption mode spectral acquisitions may beused to partition spectra based on separating lipid from non-lipidsignals and via a relationship of PG/MAXLAAL prior to classification toachieve 100% procedural success and 100% accurate diagnosis. While theinitial partition for lipid was done manually by visual signal qualityobservation believed to indicate presence or absence of lipid signalcontribution, the recognition of this may be done automatically usingseveral techniques. For example, this may be done by determininglinewidth in the LAAL region (where lipid co-exists, if present), LAALpeak amplitude exceeding a threshold, LAAL peak/power (e.g. AUC), by theability to detect a PG peak, or by the combination of any of theaforementioned techniques, as may be applied against thresholdsdetermined empirically or otherwise to represent a valid test for thesignal differentiation.

It has also been shown herein that another statistically robust hybridlinear regression equation may be used without the PG/MAXLAAL partition,at the expense of only slightly increased false negative scores (n=2).

Example 4

A DDD-MRS exam according to the DDD-MRS pulse sequence, signalprocessing, and certain diagnostic algorithm aspects of the presentdisclosure was conducted in a synthetic “phantom” spine intended tosimulate certain aspects of a lumbar spine with controlled, knownchemical environments with respect to aqueous preparations of varyingconcentrations and relative ratios between n-acetyl acetate (NAA) andlactic acid (LA) in simulated discs providing regions of interest forvoxel prescription and DDD-MRS examinations for test validationpurposes.

Materials and Methods

Sagittal plane MRI images from a GE Signa 3.0 T of the two lumbar spinephantoms shown in FIGS. 40A-B included a longitudinal series ofsimulated disc chambers along a column and floating in mineral oil. Thesimulated disc chambers were filled with buffered solutions of lithiumlactate (LA) and n-acetyl aspartate (NAA) as indicated in Table 6.Phantom “B” shown in FIG. 40A also had alternating chambers that werealso filled with mineral oil to simulate vertebral bodies (VBs), whereasthe Phantom “C” shown in FIG. 40B had the discs in immediately adjacentsuccession without intervening simulated VBs.

Voxels were prescribed within various discs among the phantoms forvaried range of target chemicals. DDD-MRS pulse sequence acquisitionsaccording to various of the present embodiments were obtained from theSigna 3T. Settings for these exams included: TR/TE settings of 1000/28ms, NSA=150, 3rd flip angle=85, voxel dimension=5×20×20 mm, VSS bandswere default width, and sweep rate=2 Kh.

Metabolite signal (Smet) for NAA was measured by integrating signalpower over a range or “bin” centered on spectral peak with width of+/−0.1 PPM. Lactate signal was measured by integrating over bin rangingfrom 0.1 PPM on either side of observed doublet peak. Unsuppressed watersignal (Suw) measured over water peak+/−0.5 PPM. Metaboliteconcentrations (CM) were then calculated using the following formulaicrelationship:CM=(Smet/Suw)×(Nw/Nmet)×C water×K;where Nw=2 H, Nmet=3 H (both NAA and LA), C water=55.5M, andK=correction factor for each phantom based on relaxation, signalmeasurement and acquisition factors. Factors underlying “K” were notcharacterized, thus K was solved for each acquisition based on knownactual concentrations of each metabolite to derive an average K valuefor each phantom which was then applied uniformly across the phantomacquisitions to solve for each CM.

Results/Discussion

Results of measured/calculated concentration values per the DDD-MRS examwere compared against known values for NAA and LA, with comparisonresults shown in FIGS. 40C-D, respectively, and in Table 6. DDD-MRSmeasured vs. known concentration comparisons resulted in very highcorrelations of R2=0.95 for the LA comparison and R2=0.93 for the NAAcomparison (after removing one clearly erroneous outlier—which residedat an exceptionally high test NAA level which is well above the typicallevels considered physiologically relevant, at least for DDD paindiagnostic purposes in the lumbar spinal discs). Ratios of NAA:LA werealso substantially accurate and significantly correlative, as shown inTable 6.

According to this study featured in Example 4, operation of the DDD-MRSsystem operation through respective modes of pulse sequence spectralacquisition, signal processing, and data extraction was verified toprovide robust results with respect to NAA and LA chemicalconcentrations, and ratios therebetween, in this controlled simulatedtest environment. This provides some degree of verification with respectto the accuracy and robust operation of the DDD-MRS system in otherapplications for performing similar operations in vivo.

Further Discussion and Additional Aspects of the Disclosure

It is to be appreciated that the present disclosure, including byreference to the Examples, provides various aspects that can be highlybeneficial, and represent new advancements that enhance the ability toperform clinically relevant MRS-based examinations of the lumbar spine,and/or of intervertebral discs, and in particular indications fordiagnosing DDD pain. Each of these aspects, taken alone, is consideredof independent value not requiring combination with other aspects hereindisclosed. However, the combination of these aspects, and varioussub-combinations apparent to one of ordinary skill, represent stillfurther aspects of additional benefit and utility. The following are afew examples of these aspects, in addition to others noted elsewhereherein or otherwise apparent to one of ordinary skill, which aspectsnonetheless not intended to be limiting to other aspects disclosedherein and are intended to be read in conjunction with the remainingdisclosures provided elsewhere herein:

Channel Selection for Data Processing and Diagnosis:

Conventional MRI systems use multi-channel acquisition coils for spinedetectors, which are pads that patients lye upon during a scan. The GESigna for example uses an 8 channel acquisition coil array, of which 6channels are typically activated for use for lumbar spine imaging anddiagnosis (including for MRS). However, the system generally combinesall data from these channels in producing a single “averaged” curve. Forsingle voxel MRS, this has been determined to be highly inefficient andsignificant source of error in the data, in particular reducingsignal-to-noise ratio. The channels vary in their geographical placementrelative to lumbar discs, and are believed to be at least one source ofvariability between them regarding acquired signal quality for a givendisc. Of the six channels, most frequently at least one of the channelsis clearly “poor” data (e.g. poor signal-to-noise), and often this canmean 2 to 5 of those channels being clearly degraded vs. one or more“strong” channels. Accordingly, the present disclosure contemplates thatcomparing the channels, and using only the “strongest” channel(s),significantly improves signal quality and thus data acquired andprocessed in performing a diagnosis. This “channel isolation/selection”is considered uniquely beneficial to the DDD pain applicationcontemplated herein, and can be done manually as contemplated herein,though the present disclosure also includes automating this operation tocompare and choose amongst the channels for a given voxel scan via anautomated DDD-MRS signal processor disclosed.

“Coherent” Averaging within and Between Channels:

During a single voxel scan, many repetitions are performed that arelater used for averaging in order to reduce noise and increasesignal-to-noise ratio in an acquired MRS spectrum. This can range fromabout 100 repetitions to about 600 or more, though more typically may bebetween about 200 to about 500, and still more frequently between about300 to about 400, and according to one specific (though example)embodiment frequently included in the physical embodiments evaluated inthe clinical study of Example 1 may be about 384 repetitions. With a TRof 1 to 2 seconds for example, this can range from less than 5 to 10minutes time.

However, a “shift” in phase and frequency has been observed among theacquired data over these repetitions. The current standard MRI systemconfigurations, via certain sequence routines, do not completely correctfor such shifts. Thus when these repetitions are averaged the resultbecomes “blurred” with reduced signal amplitude relative to noise, aswell as possibility for signal “broadening” or separation into multiplepeaks from what should be otherwise a single, more narrow band peak.

In addition or alternative to “strongest” channel selection forprocessing, significant benefit and utility is contemplated herein forcorrecting for one or both of these phase and/or frequency “shifts”among the repetitions of an acquisition series acquired at a channelduring a single voxel scan. The observed results of such processing havebeen higher signal quality, with higher signal-to-noise ratio, and/ormore narrow defined signals at bands of interest to spectral regionsassociated with chemicals believed (and correlated) to be relevant fordiagnosing disc pain (e.g., PG and/or LA and/or AL). It is noted, andrelevant to various of the detailed embodiments disclosed herein, thatthe spectral peak region associated with water is typically the mostprominent and highest amplitude signal across the spectrum. This peakand its location relative to a baseline is used according to certain ofthe present embodiments to define a given shift in a signal, and thusthat shift at the water region is used to correct the entire spectralsignal back to a defined baseline. As water peak shifts, or converselyis corrected, so does the rest of the spectrum including the targetchemical markers relevant to conducting diagnoses.

This degree and location of the water peak may also be used to determineand edit acquisition frames which are sufficiently abnormally biasedrelative to the other acquisition frames to adversely impact spectraldata (or unable to “grab and shift”), e.g. frame editing according tofurther embodiments.

Where water is not as prominent, e.g. highly desiccated discs with oversuppressed water in the sequence, other reliably prominent andrecognizable peaks maybe identified used for similar purpose (e.g. peakswithin the PG and/or LA and/or AL regions themselves). However, due toits typical prominence and many benefits of using the water peak forthese various signal processing purposes, novel approaches and settingsfor water suppression are contemplated and disclosed herein. Thisprovides for a water signal, either manually or automatically, within anamplitude range that is sufficient to locate and “grab” for processing,but not so extensive to “washout” lower chemical signatures in aninappropriate dynamic range built around the higher water signal. Theresult of corrections contemplated herein aligns the repetitions tophase and/or frequency coherence, and thus the resulting averagingachieved is desirably more “coherent” averaging. It is furthercontemplated that these shifts may be observed and corrected in eithertime or frequency domain (especially regarding frequency shift), andwhile certain embodiments are described herein in detail correctionsyielding similarly improved results may be made in either domain (againesp. re: frequency coherent correction).

DDD-MRS Factors, Criteria and Thresholds for Diagnostic Results

The present disclosure provides an empirically derived relationshipbetween four weighted factors that involve data derived from threeregions of MRS spectra acquired from discs that are generally associatedwith three different chemicals, namely PG, LA, and AL. Other supportexists to suspect these identified chemicals may be active culprits indisc pain, e.g. reducing PG, and increasing LA and AL, as factored inthe diagnostic relationship developed and applied herein. More directly,at least a sub-set of these factors used in this diagnostic developedrelationship have been directly correlated to disc pain (e.g. PG/LAratio per prior 11T studies performed ex vivo). These factors arefurther addressed in view of further supporting literature anddisclosures, which are believed to support their correlation to pain, asfollows.

The normal intervertbral disc is avascular and disc cells function underanaerobic conditions. (Ishihara and Urban 1999; Grunhagen, Wilde et al.2006) Anaerobic metabolism, such as in the setting of oxygen deprivationand hypoxia, causes lactate production. (Bartels, Fairbank et al. 1998;Urban, Smith et al. 2004) Disc pH is proportional to lactateconcentration. (Diamant, Karlsson et al. 1968) Lactic acid produces painvia acid sensing ion channels on nociceptors. (Immke and McCleskey 2001;Sutherland, Benson et al. 2001; Molliver, Immke et al. 2005; Naves andMcCleskey 2005; Rukwied, Chizh et al. 2007) Disc acidity has beencorrelated with pre-operative back pain. (Diamant, Karlsson et al. 1968;Nachemson 1969; Keshari, Lotz et al. 2008)

Proteoglycan content within the nucleus pulposus, which is the primarymatrix which holds water in the disc nucleus, decreases with discdegeneration, which is also associate with dehydration e.g. via“darkened” disc nuclei seen on T2 MRI. (Roughley, Alini et al. 2002;Keshari, Lotz et al. 2005; Keshari, Zektzer et al. 2005; Roberts, Evanset al. 2006) ChondVOItin sulfate proteoglycans inhibit nerve ingrowth.(Zuo, Hernandez et al. 1998; Zuo, Neubauer et al. 1998; Jones, Sajed etal. 2003; Properzi, Asher et al. 2003; Jain, Brady-Kalnay et al. 2004;Klapka and Muller 2006) Nerve ingrowth is increased in degenerativepainful discs. (Brown, Hukkanen et al. 1997; Coppes, Marani et al. 1997;Freemont, Peacock et al. 1997; Freemont, Watkins et al. 2002)

Discography is the current gold-standard of diagnostic care fordifferentiating painful discs, but is controversial due to being:invasive, painful, subjective, technique/operator dependent, frequentlychallenged due to high false positive rates (principally as indicated instudies with asymptomatic volunteers), and risky to the patient.(Carragee and Alamin 2001; Guyer and Ohnmeiss 2003; O'Neill andKurgansky 2004; Cohen, Larkin et al. 2005; Carragee, Alamin et al. 2006;Carragee, Lincoln et al. 2006; Buenaventura, Shah et al. 2007; Wichman2007; Derby, Baker et al. 2008; Scuderi, Brusovanik et al. 2008; Wolferet al., Pain Physician 2008; 11:513-538 ISSN 1533-3159, Derby et al.,2008) The prevailing modern guidelines for performing discographygenerally require concordant pain intensity scores equal to or above 6(on increasing scale of 0-10), provocation pressures of no more than 50psi above opening pressure, and another negative control disc in orderto determine a “positive discogram” result for a disc. This moderntechnique has been most recently suggested to provide a higherspecificity (e.g. lower false positive) rates than previously alleged inother studies. (Wolfer et al., Pain Physician 2008; 11; 513-538 • ISSN1533-3159) However, notwithstanding this potential improvement withmodern techniques in the test's accuracy, a more recent published studyhas shown the invasive needle puncture of discography significantlyincreases disc degeneration and herniations rates. Further to thisdisclosure, these adverse affects of the discography needle puncture inthe “negative control discs” have been alleged as possible culprit inadjacent level disc disease that often affects adverse outcomesfollowing surgical treatments removing the “positive discogram” discs(e.g. fusion and/or disc arthroplasty).

Proteoglycan and lactate within discs have unique MR signatures that canbe identified and objectively measured using MR Spectroscopy, and acalculated ratio based on these measures has significantlydifferentiated painful from non-painful discs in ex vivo studies ofsurgically removed discs. (Keshari, Lotz et al. 2008) In subsequentclinical evaluation and development, the further inclusion ofalanine—related to lactate to extent of both providing biomarkers forhypoxia having reasonable suspected basis in pain cascade—has resultedin similarly accurate predictive values for the platform in vivo. In oneExample, with only 6% procedural failures to make a confident diagnosis,99% accuracy resulted and including 5/5 successes in prospectiveapplication. DDD-MRS approaches, as disclosed herein, can thusnon-invasively, painlessly, and objectively measure and quantifyproteoglycan and lactate-related signatures (and for alanine spectralregion) of intervertebral discs in vivo using a novel software upgradeto commercially available MRI systems, and a novel diagnostic algorithmbased at least in part upon these in vivo measures reliablydistinguishes painful vs. non-painful discs with a lower false positiverate predicted versus discography.

The following publications are herein incorporated in their entirety byreference thereto, and provide at least in part a bibliography ofcertain disclosures referenced above and otherwise elsewhere herein:

-   Bartels, E. M., J. C. Fairbank, et al. (1998). “Oxygen and lactate    concentrations measured in vivo in the intervertebral discs of    patients with scoliosis and back pain.” Spine 23(1): 1-7; discussion    8.-   Brown, M. F., M. V. Hukkanen, et al. (1997). “Sensory and    sympathetic innervation of the vertebral endplate in patients with    degenerative disc disease.” J Bone Joint Surg Br 79(1): 147-53.-   Buenaventura, R. M., R. V. Shah, et al. (2007). “Systematic review    of discography as a diagnostic test for spinal pain: an update.”    Pain Physician 10(1): 147-64.-   Carragee, E. J. and T. F. Alamin (2001). “Discography. a review.”    Spine J 1(5): 364-72.-   Carragee, E. J., T. F. Alamin, et al. (2006). “Low-pressure positive    Discography in subjects asymptomatic of significant low back pain    illness.” Spine 31(5): 505-9.-   Carragee, E. J., T. Lincoln, et al. (2006). “A gold standard    evaluation of the “discogenic pain” diagnosis as determined by    provocative discography.” Spine 31(18): 2115-23.-   Cohen, S. P., T. M. Larkin, et al. (2005). “Lumbar discography: a    comprehensive review of outcome studies, diagnostic accuracy, and    principles.” Reg Anesth Pain Med 30(2): 163-83.-   Coppes, M. H., E. Marani, et al. (1997). “Innervation of “painful”    lumbar discs.” Spine 22(20): 2342-9; discussion 2349-50.-   Derby, R., R. M. Baker, et al. (2008). “Analgesic Discography: Can    Analgesic Testing Identify a Painful Disc?” SpineLine    (November-December): 17-24.-   Diamant, B., J. Karlsson, et al. (1968). “Correlation between    lactate levels and pH in discs of patients with lumbar    rhizopathies.” Experientia 24(12): 1195-6.-   Freemont, A. J., T. E. Peacock, et al. (1997). “Nerve ingrowth into    diseased intervertebral disc in chronic back pain.” Lancet    350(9072): 178-81.-   Freemont, A. J., A. Watkins, et al. (2002). “Nerve growth factor    expression and innervation of the painful intervertebral disc.” J    Pathol 197(3): 286-92.-   Grunhagen, T., G. Wilde, et al. (2006). “Nutrient supply and    intervertebral disc metabolism.” J Bone Joint Surg Am 88 Suppl 2:    30-5.-   Guyer, R. D. and D. D. Ohnmeiss (2003). “Lumbar discography.” Spine    J 3(3 Suppl): 11S-27S.-   Immke, D. C. and E. W. McCleskey (2001). “Lactate enhances the    acid-sensing Na+ channel on ischemia-sensing neurons.” Nat Neurosci    4(9): 869-70.-   Ishihara, H. and J. P. Urban (1999). “Effects of low oxygen    concentrations and metabolic inhibitors on proteoglycan and protein    synthesis rates in the intervertebral disc.” J Orthop Res 17(6):    829-35.-   Jain, A., S. M. Brady-Kalnay, et al. (2004). “Modulation of Rho    GTPase activity alleviates chondroitin sulfate    proteoglycan-dependent inhibition of neurite extension.” J Neurosci    Res 77(2): 299-307.-   Jones, L. L., D. Sajed, et al. (2003). “Axonal regeneration through    regions of chondroitin sulfate proteoglycan deposition after spinal    cord injury: a balance of permissiveness and inhibition.” J Neurosci    23(28): 9276-88.-   Keshari, K. R., J. C. Lotz, et al. (2005). “Correlation of HR-MAS    spectroscopy derived metabolite concentrations with collagen and    proteoglycan levels and Thompson grade in the degenerative disc.”    Spine 30(23): 2683-8.-   Keshari, K. R., J. C. Lotz, et al. (2008). “Lactic acid and    proteoglycans as metabolic markers for discogenic back pain.” Spine    33(3): 312-317.-   Keshari, K. R., A. S. Zektzer, et al. (2005). “Characterization of    intervertebral disc degeneration by high-resolution magic angle    spinning (HR-MAS) spectroscopy.” Magn Reson Med 53(3): 519-27.-   Klapka, N. and H. W. Muller (2006). “Collagen matrix in spinal cord    injury.” J Neurotrauma 23(3-4): 422-35.-   Molliver, D. C., D. C. Immke, et al. (2005). “ASIC3, an acid-sensing    ion channel, is expressed in metaboreceptive sensory neurons.” Mol    Pain 1: 35.-   Nachemson, A. (1969). “Intradiscal measurements of pH in patients    with lumbar rhizopathies.” Acta Orthop Scand 40(1): 23-42.-   Naves, L. A. and E. W. McCleskey (2005). “An acid-sensing ion    channel that detects ischemic pain.” Braz J Med Biol Res 38(11):    1561-9.-   O'Neill, C. and M. Kurgansky (2004). “Subgroups of positive discs on    discography.” Spine 29(19): 2134-9.-   Properzi, F., R. A. Asher, et al. (2003). “Chondroitin sulphate    proteoglycans in the central nervous system: changes and synthesis    after injury.” Biochem Soc Trans 31(2): 335-6.-   Roberts, S., H. Evans, et al. (2006). “Histology and pathology of    the human intervertebral disc.” J Bone Joint Surg Am 88 Suppl 2:    10-4.-   Roughley, P. J., M. Alini, et al. (2002). “The role of proteoglycans    in aging, degeneration and repair of the intervertebral disc.”    Biochem Soc Trans 30(Pt 6): 869-74.-   Rukwied, R., B. A. Chizh, et al. (2007). “Potentiation of    nociceptive responses to low pH injections in humans by    prostaglandin E2.” J Pain 8(5): 443-51.-   Scuderi, G. J., G. V. Brusovanik, et al. (2008). “A critical    evaluation of discography in patients with lumbar intervertebral    disc disease.” Spine J 8(4): 624-9.-   Sutherland, S. P., C. J. Benson, et al. (2001). “Acid-sensing ion    channel 3 matches the acid-gated current in cardiac ischemia-sensing    neurons.” Proc Natl Acad Sci USA 98(2): 711-6.-   Urban, J. P., S. Smith, et al. (2004). “Nutrition of the    intervertebral disc.” Spine 29(23): 2700-9.-   Wichman, H. J. (2007). “Discography: over 50 years of controversy.”    Wmj 106(1): 27-9.-   Wolfer, L. R., R. Derby, et al. (2008). “Systematic review of lumbar    provocation discography in asymptomatic subjects with a    meta-analysis of false-positive rates.” Pain Physician 11(4):    513-38.-   Zuo, J., Y. J. Hernandez, et al. (1998). “Chondroitin sulfate    proteoglycan with neurite-inhibiting activity is up-regulated    following peripheral nerve injury.” J Neurobiol 34(1): 41-54.-   Zuo, J., D. Neubauer, et al. (1998). “Degradation of chondroitin    sulfate proteoglycan enhances the neurite-promoting potential of    spinal cord tissue.” Exp Neurol 154(2): 654-62.    Notwithstanding the foregoing, it is to be appreciated that despite    the support for suspecting these chemicals as the cause of pain, and    despite the belief that these chemicals are measured and represented    at least in part by the data derived from the MRS data acquired,    this correlation need not be accurate in order for the data and    diagnostic algorithm and approach presented herein to remain valid    and highly useful.

In particular regard to MRS data derived from regions associated with LAand AL, these are quite narrowly defined ranges closely adjacent to eachother, and also overlap with a much broader band associated with lipid.Accordingly, the data acquired from these two “bins” may blur betweenthe actual two chemical sources. However, as they both relate to and area product of abnormal cellular metabolism and hypoxia, their combinationmay be fairly considered a signature region more broadly for “abnormalcellular metabolism/hypoxia.” Furthermore, lipid contribution may biasmeasurements in this region, and as lipid is a high molecular weightmolecule if present the signal is typically strong and often may washout resolution of either or both of LA or AL-based signal in the region.However, in the current experience with DDD-MRS, even where lipid signalis believed present, and even in significant degree, the acquired dataintended to represent LA and AL as processed through the diagnosticalgorithm and processor has not produced a false result against controls(e.g. remains an accurate result). When this happens, the diagnosticresult is consistently MRS+ indicating a positive result for pain in thesuspect disc. However, such lipid-related positive results occur mostfrequently in L5-S1 discs that are associated with a particulardegenerative profile and morphology that is more reliably diagnosed aspainful on MRI alone (and consistently confirmed as such via PD).

To the extent the measurements derived from the MRS “regions” believedto be associated with these chemicals, and as used in the weightedfactor diagnostic algorithm developed, are applied uniformly across thedifferent control disc populations, the diagnostic accuracy of theresult prevails in the ultimate comparison data—regardless of the sourceof the MRS data acquired. Accordingly, the benefit and utility of thediagnostic approach is defined ultimately by its diagnostic results, andnot intended to be necessarily limited and defined only by the theory asto what the underlying sources of the measured signatures are.

Conversely, it is also further contemplated and to be understood thatthe present disclosure provides a specific diagnostic relationshipalgorithm that produces a particular range of diagnostic results thatcompare with high correlation with control measures for pain/non-pain indiscs evaluated. However, this is the result of statistically generatedcorrelation and retrospective approach to data fitting. Whileappropriate for diagnostic algorithm development and the specific resultdisclosed herein is considered highly beneficial, this may migrate toother specific algorithms that may be more preferred though withoutdeparting from the broad scope intended for the various aspects of thisdisclosure. Such modifications may be the result of further dataprocessing across more samples, for example, and may affect the“weighting” multipliers associated with each factor used in thealgorithm, or which factors are featured in the algorithm, or whichregions or features of the MRS spectra are even used as the signaturesfrom which data is derived and used in the algorithm. This has beendemonstrated by way of the Examples 1-3 provided herein, and whereinthree different specific diagnostically relevant and viable approachesare presented and described for similar data sets (e.g. in particularcomparison between Examples 2 and 3 of the same clinical data set).

It is contemplated that while the DDD-MRS diagnostic processor hereindisclosed and diagnostic results provided therefrom, as disclosed incontext of clinical data presented under Example 1 (and late by Examples2 and 3), provide binary MRS+ and MRS-results for severe pain andabsence of severe pain in discs, respectively. However, the results arealso quantified along a scaled range which may be appropriatelyinterpreted by a diagnostician as “levels” of relevance along thepain/non-pain range. Such interpretation may impact the direction ofpain management decisions, such as which discs to treat, how to treat,or not to treat at all. One example of such other way of presentingDDD-MRS diagnostic information for utility to appropriate clinicians isdemonstrated by reference to the “% prediction painful” presentation ofdata shown and discussed herein (which may be instead or in combinationalso determined and presented as “% prediction non-painful”). Moreover,while the current diagnostic embodiments have been described byreference to site-specific locations of pain sources at reference discs,diagnostic value may be more generalized to confirmed presence orabsence of any painful disc at all. Such may impact more generalmanagement decision, such as administration or avoidance of painmedication. Still further, the current aspects may be used to assessaspects of the chemical environments of discs, either in addition to oralternative to specific diagnostic indications such as for pain ornon-pain determinations for given discs. This may be effectivelyutilitarian for example by providing measures of chemical biomarkers,such as PG, LA, AL, LAAL, etc., such as amounts or concentrationsthereof in the tissues (and/or ratios). This may be relevant for examplein other indications or applications, such as research purposes (e.g.biologics or cell therapy approaches to treating or providingprophylaxis to discs). This may be useful either prior to treatment,and/or following treatment to assess certain aspects of outcomes andprogression of the treatment or underlying disease or condition intendedto be treated (as may relate to chemicals being monitored).

Furthermore, in still further embodiments, the diagnostic results may beprovided in different forms than as described by the specificembodiments disclosed by reference to a particular example, such asExample 1 for example. For example, binary definitive diagnoses of MRS+and MRS− may be supplemented with “indeterminate” as a third category.This may, for example, represent a result of applying certain thresholdcriteria that must be met in order to make a definitive +/−determination. Such criteria may include, for example, SNR threshold ofthe underlying post-processed DDD-MRS spectrum from which the diagnosticdata is extracted for performing the diagnoses. In another example, adefined proximity of calculated diagnostic results from the DDD-MRSdiagnostic processor to the zero (0) median threshold between MRS+ andMRS− diagnoses may represent a threshold under which definitive MRS+/−determination is not decidedly made by the processor.

It is also to be further appreciated that the pulse sequence platformapproach, and/or specific parameter settings, and/or signal processingapproaches (and/or parameter or threshold criteria settings), may bemodified. Such modifications may affect resulting spectra (and dataextracted therefrom) sufficiently to redistribute the regional data usedfor diagnostic purposes, and may thus motivate or necessitate are-evaluation and re-formation of the diagnostic algorithm that isappropriate for data acquired and/or processed under those modifiedapproaches. Accordingly, while the present interactions between thesecomponent parts of an overall DDD-MRS system, and results, areconsidered of particular benefit for forward application in clinicaluse, such further modifications are also considered to fall within thebroad scope of the aspects disclosed herein, and may represent forexample a consequence of further development and experience as would beapparent to one of ordinary skill (though such further modifications mayalso provide still further benefit).

L5-S1 and Novel Detection Coils:

The L5-S1 disc is typically oriented at an oblique angle relative toother lumbar discs, and has unique shape that in many circumstanceschallenges the ability to prescribe voxel for adequate DDD-MRS dataacquisition. The current voxelation plan for MRS generally requires athree-dimensional “cube” of space to be defined as the voxel (a pixelwith volume), typically done by an operator technician on overlay to MRIimages of the region. However, for this angled L5-S1 disc, the voxelvolume may be maximized by angling the voxel to match the angulateddisc. However, such angled voxels at this location have been observed torelate to degraded data acquisition by existing spine detector coils.Accordingly, a custom spine coil is further contemplated that angles atleast one coil channel to either a pre-determined angle morerepresentative of typical L5-S1 discs, or a range of angles may beprovided by multiple such coils in a kit, or the coil channel may begiven an “adjustable” angle to meet a given anatomy. Furthermore,software may be adapted to identify an angled voxel and modify thecoordinate system assigned for sequence and/or multi-channel acquisitionin order better acquire data from an angled voxel (e.g. where planarslices are taken through the voxel as data acquired, the planarcoordinates are revised into an adjusted coordinate system that accountsfor the angulation relative to the data acquisition at the channel(s)).This uniquely angled disc level is also associated with and locatedwithin a radiused curvature at the small of the back, which may be moreextreme in some patients than others. While simply adjusting the angleof lower detection channel coils may improve acquisition here, furthermore dramatic variations are also contemplated. In one such furtheraspect, a detector coil array is created with smaller coils, and/or on aflexible platform that is adjusted to more accurately fit against thelower back (vs. a planar array currently used, but for curved lowerspine with increasingly angulated discs toward the lower lumbar andsacral regions). Further to this approach, the relative locations andorientations of the detector coils may be sensed, with proper coordinatesystem assigned thereto for sequencing and acquisition during singlevoxel MRS of the spine (especially intervertebral discs), and which alsomay be adapted relative to coordinates of voxel orientation, dimensions,and shape.

T1-Rho:

An additional MRI-based pulse sequence technology has been previouslydisclosed called “T1-Rho”. This is a sequence that has been alleged fordetecting, measuring, and indicating the amount (e.g. concentration) ofproteoglycan, via n-acetyl or n-acetyl acetate, in tissue, andfurthermore for using this information for diagnostic benefit for someconditions. In one particular regard, this has been alleged to bepotentially useful for monitoring degree of degeneration, in thatreduced proteoglycan in discs may correlate to advancing degree ofdegeneration. While pain correlation with proteoglycan variability hasnot been determined, the ration of PG to other metabolites, such as forexample Lactate (and/or alanine), is believed to be a consistent andpotent indicator for localized discogenic pain. Accordingly, the presentdisclosure combines T1-Rho with other measurements, e.g. MRSmeasurements, in evaluating tissue chemistry for purpose of performing adiagnosis. In one particular mode contemplated herein, the T1-Rhomeasurement of proteoglycan/n-acetyl content is used to “normalize” orotherwise calibrate or compare an MRS measurement of that relatedregion. In doing so, other metabolites in the MRS spectrum may be alsocalibrated for more accurately calculated “concentration” measurement.This calibration may be done in evaluating MRS signal quality, such asfor example between channels or within a channel itself, and MRS data isused for the diagnosis. In a further mode, T1-Rho information related toPG may be used as the data for that chemical constituent in tissue, anddata for another diagnostically relevant chemical, e.g. Lactate asmeasured for example via MRS (or other modality), may be used incombination with the PG measurement in an overall diagnostic algorithmor evaluation. Such algorithms applied for diagnostic use may beempirically driven based upon experimental data which may be conductedand acquired by one of ordinary skill for such purpose based upon thisdisclosure. For example, a database of sufficient patient data based onT1-rho measurements (for proteoglycan) and MRS measurements (such as forPG and/or Lactate, for example) may be correlated in a multi-variatelogistic regression analysis against other pain indicators such asprovocative discography or treatment outcomes, resulting in a highlycorrelative algorithm based upon the data fit. This may then be usedprospectively in predicting or assessing localized pain in newlyevaluated patient tissues. In one particular benefit, MRS techniquesinclude particular sequence parameters that emphasize lactate forimproved lactate-related data extraction, and decreasing lipid artifact(which often overlays over lactate to confound lactate data collection),but not considered as robust for other chemicals, such as potentiallyPG/n-acetyl. One such technique extends the time delay from magneticactivation to data collection, thus increasing overall time forrepetitive scans. However, T1-Rho is relatively fast to perform relativeto MRS. Accordingly, one particular further embodiment uses T1-rho forPG measurement, and MRS as enhanced for lactate measurement, andcombines this data into an empirically data-driven algorithm forperforming a diagnosis. Moreover, a further aspect contemplated hereinuses T1-rho for PG measurement, in combination with pH or pO2measurement (e.g. via a sensor on a needle, such as a discographyneedle) to monitor local acidity in the disc (also believed to relate tolactate concentration).

Diagnostic Display “Enhancing” MRI Images

The various aspects, modes, and embodiments of the present disclosureprovide, among other beneficial advancements, a significant enhancementand improvement to standard MRI for locally diagnosing painful and/ornon-painful discs. The utility of each of these diagnoses—painful, andnon-painful—is of independent value on its own. While indicating a discis definitively painful may often augment other clinical or diagnosticindications for directing treatment to the level, indicating a disc isdefinitively not painful also provides valuable information to exclude adisc as possible pain culprit and avoid unnecessary intervention to thelevel (especially where other clinical or diagnostic indications mayindicate another level as painful, but not provide definitive answer tothe other level/s). This is for example often the case with respect toL3-L4 and L4-L5 discs, where L5-S1 discs (most prevalently painful amongthe levels) may often be already suspect per MRI and other indications,but the higher adjacent disc levels are indeterminate.

The present aspects have been presented in terms of physical embodimentsevaluated in clinical study with highly accurate results againstcontrols. By providing a non-invasive alternative to discography aspresented by these present embodiments, even if diagnosticallyequivalent, significant benefits are advanced by avoiding morbidity,pain, and other inefficiencies and downsides associated with thatinvasive test.

As an enhancement to MRI, further aspects of the present disclosureprovide useful diagnostic display to indicate the results in overlaycontext onto the MRI image itself and providing context to thestructures revealed therein, such as for example as shown in FIGS. 32A-Bfor two different patients receiving a DDD-MRS diagnostic exam accordingto Example 1.

It is to be appreciated by one of ordinary skill that the variousaspects, modes, embodiments, features, and variations of the presentdisclosure include, without limitation, the following.

One aspect of the present disclosure is a MRS pulse sequence configuredto generate and acquire a diagnostically useful MRS spectrum from avoxel located principally within an intervertebral disc of a patient.According to one mode of this aspect, the pulse sequence is configuredto generate and acquire the MRS spectrum from a single voxel principallylocated within the disc. According to another mode of this aspect, thepulse sequence is configured to generate and acquire the MRS spectrumfrom the voxel located principally within a nucleus of the disc.According to another mode of this aspect, the pulse sequence isconfigured to generate and acquire the MRS spectrum with sufficientsignal-to-noise ratio (SNR) upon appropriate post-signal processing toperform at least one of: detect and measure at least one chemicalconstituent within the disc; and diagnose a medical condition based uponone or more identifiable signal features along the spectrum. Accordingto another mode, the pulse sequence is configured to generate andacquire the MRS spectrum from a single voxel principally located withina nucleus of the disc. According to another mode, the pulse sequence isconfigured to generate and acquire the MRS spectrum from a voxelprincipally located within an intervertebral disc of the lumbar spine.According to another mode, the pulse sequence is configured to generateand acquire at least one MRS spectrum from at least one voxelprincipally located within at least one of L3-L4, L4-L5, and L5-S1intervertebral discs. These discs are the most predominant discsimplicated by chronic, severe low back pain, and are also characterizedby typically larger disc spaces than other higher disc levels and thusmore conducive to single voxel spectroscopy (though not necessarily solimited to only these discs in all cases). According thus to anothermode, however, the pulse sequence is configured to generate and acquiremultiple MRS spectra from multiple voxels, respectively, principallylocated within each of L3-L4, L4-L5, and L5-51 intervertebral discs.

According to another mode, the pulse sequence is configured to generateand acquire multiple MRS spectra from multiple voxels, respectively,principally located within each of L3-L4, and L4-L5 intervertebraldiscs. These discs are typically less oblique than L5-S1 disc, and thusrepresent different geometric, and perhaps in certain circumstancesdifferent biomechanical and/or biochemical, environments vs. typicallymore oblique L5-S1 disc, and thus may represent unique optimalapproaches for diagnostic application of the present embodiments versusfor the L5-S1 disc. According to one embodiment of this mode, the discsare substantially non-oblique, such as for example as may be relative toa relatively more oblique L5-S1 adjacent thereto. According thus to yetanother mode, the pulse sequence is configured to generate and acquirethe MRS spectrum from the voxel located principally within the L5-S1intervertebral disc. As stated above, this disc level may at timespresent unique considerations relative to other lumbar discs that areaddressed with unique relative approaches versus other lumbar discs.According to one embodiment of this mode, the disc is substantiallyoblique, such as for example relative to adjacent lumbar disc segmentsabove this level. According to another mode, the pulse sequence isconfigured to operate in a first mode for a substantially non-obliquedisc, and a second mode for a substantially oblique disc.

The present disclosure is considered readily adaptable to operate on andwith multiple different specific MR systems, including of differentrelative field strengths and as may be made available and operate inrelative custom formats from various different manufacturers, though asmay be custom developed by one of ordinary skill for compatibility andoptimal functionality for intended use on and with any particular MRsystem or category (e.g. field strength). According to another modetherefore, the pulse sequence of the various aspects of the presentdisclosure is configured to generate and acquire the MRS spectrum via anNMR system of at least about 1.2 tesla (T) field strength. According toanother mode, the pulse sequence is configured to generate and acquirethe MRS spectrum via an NMR system of about 1.2 tesla (T) fieldstrength. According to another mode, the pulse sequence is configured togenerate and acquire the MRS spectrum via an NMR system of at leastabout 1.5 tesla (T) field strength. According to another mode, the pulsesequence is configured to generate and acquire the MRS spectrum via anNMR system of about 1.5 tesla (T) field strength. According to anothermode, the pulse sequence is configured to generate and acquire the MRSspectrum via an NMR system of at least about 3.0 tesla (T) fieldstrength. According to another mode, the pulse sequence is configured togenerate and acquire the MRS spectrum via an NMR system of about 3.0tesla (T) field strength. According to another mode, the pulse sequenceis configured to generate and acquire the MRS spectrum via an NMR systemof about 7.0 tesla (T) field strength. According to another mode, it isto be appreciated that the pulse sequence is configured to generate andacquire the MRS spectrum via an NMR system in the range of about 1.2 toabout 7.0 tesla (T) field strength. According to another mode, the pulsesequence is configured to generate and acquire the MRS spectrum via anNMR system in the range of about 1.2 to about 3.0 tesla (T) fieldstrength. According to another mode, the pulse sequence is configured togenerate and acquire the MRS spectrum via an NMR system in the range ofabout 1.5 to about 3.0 tesla (T) field strength. While these ranges andspecific field strengths noted represent existing systems available onthe market today, or at least under investigation (e.g. 7.0 T), it isfurther contemplated that other systems outside this range may also besuitable. However, it is also to be appreciated that systems below about1.5 or 1.2 Tesla may be challenged with respect to signal:noise ratio inmany circumstances (though may nonetheless be operable suitably asintended in others). Furthermore, current experience has revealed thatacquisitions following the DDD-MRS aspects of the present disclosure maybe similarly robust when conducted with field strength as low as 1.5 Tversus as acquired via higher 3.0 T systems (such as used in theExamples). Moreover, systems above about 3.0 T or 7.0 T may presentsignificant safety concerns for many applications (though again maynonetheless suitable for others).

Certain pulse sequence modes of the present aspects of the disclosureare also to be appreciated as providing particular benefit for certainintended uses, including those featured specifically herein such as viathe Examples. According to one such mode of the present aspects, thepulse sequence comprises a chemical shift selective (CHESS) sequence.According to another mode, the pulse sequence comprises a point resolvedspectroscopy (PRESS) sequence. According to another mode, the pulsesequence comprises a combination CHESS-PRESS sequence. According toanother mode, the pulse sequence comprises a combination CHESS-VSS-PRESSsequence. According to another mode, the pulse sequence comprises atleast one control variable (CV) parameter setting as disclosed inTable 1. According to another mode, the pulse sequence comprises all thecontrol variable (CV) parameter settings disclosed in Table 1. Accordingto another mode, the pulse sequence comprises an echo time (TE) in therange of about 25 to about 40 milliseconds. According to another mode,the pulse sequence comprises an echo time of about 28 milliseconds. Thisspecific setting, while not intended to be necessarily limiting to broadintended scope of the present aspects and modes, has been observed toprovide sufficiently robust results as intended for various uses, suchas according to the Examples. According to another mode, the pulsesequence comprises a repetition time (TR) in the range of about 750 toabout 2000 milliseconds (2 seconds). According to another mode, thepulse sequence comprises a repetition time (TR) of about 1000milliseconds. According to another mode, the pulse sequence comprises arepetition time of about 750 milliseconds and is configured to operatewith an MR system with a field strength of between about 1.2 T and about1.5 T. This has been observed, for example in one particular embodiment,to be particularly beneficial for 1.5 T MR applications. According toanother mode, the pulse sequence comprises a repetition time of betweenabout 1000 and about 1500 milliseconds and is configured to operate withan MR system with a field strength of between about 3 T and about 7 T.According to another mode, the pulse sequence is configured to adjustthe repetition time (TR) with respect to the field strength of the MRsystem, which may be automatic in one beneficial variation, or manuallyset to adjust accordingly. It is to be appreciated that these settingsfor TR present a certain trade off with respect to time required tocomplete a pulse sequence acquisition series, and thus sufficientlyshort times to provide adequate signal quality may be optimized for timeefficiency, though longer times may be done if time is available or notof essence. Time, however, may be a significant consideration in manycircumstances, such as for example for efficiency in conducing the examin MR imaging center setting, and also patient comfort, in addition tolonger times for exams increase opportunities for patient motionartifact etc. that could compromise results (to extent not countered bythe various signal processing aspects of the present disclosure).

According to another mode, the pulse sequence comprises an acquisitionmatrix size setting of about 1 in each dimension, with a number ofspatial slices setting of 1.

Relative degree of water signal in DDD-MRS pulse sequence acquisitionsmay be relevant to the ability to fully signal process such signals asintended by various aspects of the present disclosure, and thus certainaspects related to water suppression and water signal control aredisclosed herein and to be appreciated with respect to the pulsesequence. According to another mode, the pulse sequence is configured togenerate and acquire a repetitive frame MRS acquisition series from thevoxel with signal-to-noise ratio (SNR) in the water region along thespectrum of multiple said frames that is sufficiently high to beidentified, yet sufficiently low to provide adequate dynamic range withsufficient signal-to-noise ratio (SNR) along other chemical regions ofdiagnostic interest along the spectral frames to allow the other regionsto be identified and evaluated, post-signal processing andpost-averaging of the frames, for diagnostic use. Suppressed watersignal, and control of it via the pulse sequence settings, varied overtime of development across the clinical data set featured among theExamples 1-3 disclosed herein. However, as demonstrated via the highlyrobust ultimate results these ranges of water suppression controlexperienced were observed to provide sufficiently adequate results inmost cases. This experience ranged between 45 and 125 degrees for 3^(rd)CHESS flip angle, with an average of about 120 degrees (plus/minus about30 degrees standard deviation). However, these settings for eachacquisition are discrete, and upon achieving sufficient results a chosensetting was cast for a given acquisition. The majority of acquisitionsare believed sufficient at about 85 to about 100 degrees for this thirdCHESS flip angle, though again may be custom set in iterative experienceor via automated feedback control based upon trial and error in measuredsignal quality.

According nonetheless to another mode of the present aspects, the pulsesequence comprises a third CHESS flip angle of at least about 45degrees. According to another mode, the pulse sequence comprises a thirdCHESS flip angle of at least about 65 degrees. According to anothermode, the pulse sequence comprises a third CHESS flip angle of up toabout 145 degrees. According to another mode, the pulse sequencecomprises a third CHESS flip angle of up to about 125 degrees. Accordingto another mode, the pulse sequence comprises a third CHESS flip angleof between about 45 and about 145 degrees. According to another mode,the pulse sequence comprises a third CHESS flip angle between about 65and about 125 degrees. According to another mode, the pulse sequencecomprises a third CHESS flip angle that is adjustable based upon adegree of water observed in the region of interest. According to oneembodiment of this mode, the degree of water is observed according to aprior test pulse sequence. According to one embodiment of this mode, thepulse sequence is configured to operate in series following the priortest pulse sequence in a common MR exam session, and the third CHESSflip angle is automatically adjustable based upon the observed degree ofwater in the prior test pulse sequence. According to another embodiment,the third CHESS flip angle is automatically adjustable based upon aT2-weighted acquisition value for the region of interest. According toanother embodiment, the third CHESS flip angle is automaticallyadjustable to a value determined based upon an empirical correlationbetween third CHESS flip angle and T2-weighted acquisition value for theregion of interest according to a prior acquisition data set. Accordingto another mode, the pulse sequence comprises at least one of thefollowing CHESS flip angles: about 105 degrees (angle 1); about 80degrees (angle 2); about 125 degrees (angle 3). In some embodiments, thefirst CHESS flip angle can be between about 60 degrees and about 180degrees, or between about 85 degrees and about 125 degrees. In someembodiments, the second CHESS flip angle can be between about 60 degreesand about 180 degrees, or between about 65 degrees and about 105degrees. In some embodiments, the third CHESS flip angle can be betweenabout 45 degrees and about 145 degrees, or between about 85 degrees andabout 125 degrees, or between about 105 degrees and about 145 degrees.

Certain aspects are also disclosed related to a PRESS mode of operation.According to one such example mode, the pulse sequence comprises PRESScorrection settings of about 1.2 for each of X, Y, and Z axes. OtherPRESS correction settings can be used, such as values greater than 1.0and less than about 1.5. According to another mode, the pulse sequencecomprises at least one of the following PRESS flip angles: about 90(angle 1); about 180 (angle 2); about 180 (angle 3). According toanother mode, either or both of the second and third PRESS flip anglesmay be between about 150 and about 180 degrees, and in one particularembodiment may be for example about 167 degrees. As flip angle generallycorrelates with time required to conduct the exam, signal qualityresults may be optimally determined empirically against different flipangles, and it may also be the case that a setting (e.g. 180) may not bethe exact flip angle actually deployed (e.g. may actually be different,e.g. about 167 for example).

According to another mode of the present MRS pulse sequence aspects, thepulse sequence is provided in combination with an MRS signal processoraccording to one or more of the various aspects, modes, embodiments,variations, and or features thereof as otherwise elsewhere hereinprovided.

Another aspect of the present disclosure is thus an MRS signal processorconfigured to process spectral data from an MRS pulse sequence.

According to one mode of this aspect, the MRS signal processor comprisesa channel selector that is configured to select a sub-set of multiplechannel acquisitions received contemporaneously from multiple parallelacquisition channels, respectively, of a multi-channel detector assemblyduring a repetitive-frame MRS pulse sequence series conducted on aregion of interest within a body of a subject. According to oneembodiment of this mode, the channel selector of the MRS signalprocessor is configured to select a sub-set of multiple channelacquisitions received contemporaneously—from multiple parallelacquisition channels, respectively, of a multi-channel detector assemblyduring the repetitive-frame MRS pulse sequence series conducted on avoxel principally located within an intervertebral disc within the bodyof the subject. According to another embodiment, the channel selector ofthe MRS signal processor is configured to automatically differentiaterelatively stronger from weaker channel acquisitions received. Accordingto another embodiment, the channel selector of the MRS signal processoris configured to determine and select a strongest single channelacquisition signal among the multiple channel acquisitions. According toanother embodiment, the channel selector of the MRS signal processor isconfigured to determine and select the strongest single channelacquisition based upon a highest measured parameter of the singlechannel acquisition spectral series comprising at least one ofamplitude, power, or signal-to-noise ratio (SNR) of water signal in thespectrum in the selected channel relative to the other channel.According to one highly beneficial variation of this embodiment, thechannel selector of the MRS signal processor is configured to determineand select the strongest single acquisition channel with CHESS sequencedisabled. According to another beneficial variation of this embodiment,the channel selector is configured to perform a channel selection thatis based upon a frame averaged spectrum of the series acquired from thechannel. According to one beneficial alternative feature of thisvariation, the frame averaged spectrum of the series is acquired withthe CHESS disabled on unsuppressed water frames. According to anothervariation of this embodiment, the channel selector of the MRS signalprocessor is configured to determine and select a sub-set of strongestchannels based upon a range threshold based from the highest measuredparameter of the strongest single channel. According to anotherembodiment, the channel selector of the MRS signal processor isconfigured to determine and select one or more “strongest” channelsamong the series based upon a threshold criteria for a feature of thechannel acquisition data. According to one beneficial variation of thisembodiment, the one or more strongest channels is determined andselected by averaging the first unsuppressed water frames for eachchannel (with CHESS disabled) and measuring the signal to noise ratio(SNR) of the unsuppressed water signal, determine which channel has thestrongest SNR and then selecting those additional channels that fallwithin a threshold range, e.g. about 3 dB (or may be for example a rangeof 1 to 6 dB) of the channel with the strongest SNR. According to stillfurther channel selector embodiments, the channel selector is providedin combination with one or more of the various other aspects, modes,embodiments, variations, and features related to other MRS pulsesequence and/or MRS signal processor disclosures provided herein.

Another mode of the MRS signal processor aspects of the presentdisclosure comprises a phase shift corrector configured to recognize andcorrect phase shifting within a repetitive multi-frame acquisitionseries acquired by a multi-channel detector assembly during an MRS pulsesequence series conducted on a region of interest within a body of asubject. According to one embodiment of this mode, the phase shiftcorrector is configured to recognize and correct the phase shiftingwithin a repetitive multi-frame acquisition series acquired by amulti-channel detector assembly during an MRS pulse sequence seriesconducted on a voxel within an intervertebral disc in the body of thepatient. According to another embodiment, the phase shift corrector isconfigured to recognize and correct the phase shifting in the timedomain. According to another embodiment, the phase shift corrector isprovided in combination with one or more of the various other aspects,modes, embodiments, variations, and features related to other MRS pulsesequence and/or MRS signal processor disclosures provided herein.

Another mode of the MRS signal processor aspects of the presentdisclosure comprises a frequency shift corrector configured to recognizeand correct relative frequency shifts between multiple acquisitionframes of a repetitive multi-frame acquisition series acquired within anacquisition detector channel of a multi-channel detector assembly duringa MRS pulse sequence series conducted on a region of interest within abody of a subject. According to one embodiment of this mode, thefrequency shift corrector is configured to recognize and correctfrequency shift error between multiple acquisition frames of arepetitive multi-frame acquisition series acquired within an acquisitiondetector channel of a multi-channel detector assembly during a MRS pulsesequence series conducted on a voxel within an intervertebral disc inthe body of the subject. According to another embodiment, the frequencyshift corrector is configured to recognize and correct the frequencyshift error in the time domain. According to one beneficial example ofthis embodiment, the frequency shift is recognized and corrected in thetime domain by the application of the inverse of a 1^(st) order linearcurve fit of the incremental phase estimate of time domain informationin the 16 frame average of unsuppressed water frames (such as forexample about 16 unsuppressed water frames of the detailed illustrativeembodiments and Examples disclosed herein). According to anotherembodiment, the frequency shift corrector is configured to recognize andcorrect the frequency shift error in the frequency domain. According toone beneficial example of this embodiment, the frequency shift isrecognized and corrected in the frequency domain by transforming thetime domain information in the unsuppressed water frames (e.g. n=16)into the frequency domain to locate the water signal peak, determine thefrequency error of the water peak, and then shift the transformedsuppressed water frames by the negative of the frequency error.According to another example, the frequency shift corrector isconfigured to identify and locate a water signal in each of multipleacquisition frames of the series, compare the location of the locatedwater signals against a reference baseline location to determine aseparation shift therebetween for each frame, and to correct the shiftto align the location to the baseline location by applying anappropriate offset to all the spectral data of each frame. According toone variation of this example, the location of the water signal isestimated based upon a location range where the water signal exceeds athreshold amplitude value. According to another variation, the watersignal identified and located comprises a peak value of the watersignal. According to one highly beneficial feature that may be furtherembodied in this variation, the threshold amplitude value is greaterthan about 0.6 and/or less than about 0.9, and the threshold amplitudevalue can be 0.8 in some cases. According to another embodiment of thismode, the frequency shift corrector is provided in combination with oneor more of the various other aspects, modes, embodiments, variations,and features related to other MRS pulse sequence and/or MRS signalprocessor disclosures provided herein.

Another yet another mode of the MRS signal processor aspects disclosedherein comprises a frame editor. According to one embodiment of thismode, the frame editor is configured to recognize at least one poorquality acquisition frame, as determined against at least one thresholdcriterion, within an acquisition channel of a repetitive multi-frameacquisition series received from a multi-channel detector assemblyduring a MRS pulse sequence series conducted on a region of interestwithin a body of a subject. According to one example of this embodiment,the frame editor is configured to edit out the poor quality frame fromthe remainder of the MRS pulse sequence series otherwise retained forfurther signal and/or diagnostic algorithm processing. According toanother embodiment, the frame editor is configured to recognize the poorquality acquisition frame based upon a threshold value applied to errorin location of recognized water signal from an assigned baselinelocation. According to another embodiment, the frame editor isconfigured to recognize the poor quality acquisition frame based upon athreshold confidence interval applied to the ability to recognize thesignal location of water signal in the frame spectrum. According to oneexample of this embodiment, the water signal location comprises alocation of a peak of the water signal. According to another example, aconfidence level for the location of the water signal peak of a frame isestimated and compared to a confidence level threshold to qualify aframe for subsequent frequency correction. According to another moredetailed example, a confidence level may be determined by the followingsteps: (1) analyze the discrete amplitude spectrum in the range of thecenter-tuned frequency plus and minus 40 Hz (in the case of a 3T system,half that for a 1.5 T system); (2) locate the highest peak and determineits width at the half-amplitude point; (3) determine the total spectralwidth of all parts of the spectrum which exceed the half-amplitude pointof the highest peak; (4) form the confidence estimate by taking theratio of the spectral width of the greatest peak divided by the totalspectral width which exceeds the threshold. By way of furtherillustration of this example, if there is only a single peak above thethreshold, the confidence estimate will be 1.0, if there are many otherpeaks or spectral components which could be confused with the greatestone, then the estimate will reduce and ultimately approach zero (0). Itis believed that this provides a simple and robust estimate of therandomness or dispersal of energy in the vicinity of the water peak.Like an entropy measure, described elsewhere herein, it has thedesirable characteristic that its performance is generally believed tobe invariant with amplitude. According to still another embodiment ofthe present mode, the frame editor is provided in combination with oneor more of the various other aspects, modes, embodiments, variations,and features related to other MRS pulse sequence and/or MRS signalprocessor disclosures provided herein.

Another mode of the MRS signal processor aspects of the presentdisclosure comprises an apodizer to reduce the truncation effect on thesampled data. The apodizer according to certain embodiments isconfigured to apodize an MRS acquisition frame in the time domainotherwise generated and acquired via an MRS pulse sequence aspectotherwise herein disclosed, and/or as also otherwise signal processed byone or more of the various MRS signal processor aspects also otherwiseherein disclosed. The apodizer according to various embodiments of thismode is provided in combination with one or more of the various otheraspects, modes, embodiments, variations, and features related to otherMRS pulse sequence and/or MRS signal processor disclosures providedherein.

It is to be further appreciated that the various MRS signal processor,aspects, modes, features, variations, and examples herein described maybe configured according to further modes to operate and/or providediagnostic information related to a tissue in a patient based upon anMRS spectrum in real-part squared representation of the acquiredspectral data or processed spectrum. According to still further modes,such may be operated upon or presented as complex absorption spectrum ofthe acquired or processed data. Yet another mode contemplated operatesand/or presents processed results as complex absorption spectrum andalso as real part squared representation of the acquired and/or signalprocessed data.

Another aspect of the present disclosure is an MRS diagnostic processorconfigured to process information extracted from an MRS spectrum for aregion of interest in a body of a subject, and to provide the processedinformation in a manner that is useful for diagnosing a medicalcondition or chemical environment associated with the region ofinterest.

According to one mode of this aspect, the MRS diagnostic processor isconfigured to process the extracted information from the MRS spectrumfor a voxel principally located in an intervertebral disc of thesubject, and to provide the processed information in a manner that isuseful for diagnosing a medical condition or chemical environmentassociated with the intervertebral disc. According to one embodiment ofthis mode, the MRS diagnostic processor is configured to process theextracted information from the MRS spectrum for a voxel principallylocated in a nucleus of the intervertebral disc, and to provide theprocessed information in a manner that is useful for diagnosing amedical condition or chemical environment associated with theintervertebral disc. According to another embodiment, the MRS diagnosticprocessor is configured to provide the processed information in a mannerthat is useful for diagnosing the intervertebral disc as painful.According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as severely painful.According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as not severely painful.According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as substantiallynon-painful. According to another embodiment, the MRS diagnosticprocessor is configured to diagnose the disc as painful. According toanother embodiment, the MRS diagnostic processor is configured todiagnose the disc as severely painful. According to another embodiment,the MRS diagnostic processor is configured to diagnose the disc as notseverely painful. According to another embodiment, the MRS diagnosticprocessor is configured to diagnose the disc as substantiallynon-painful. According to another embodiment, the MRS diagnosticprocessor is configured to diagnose the disc with respect to %probability the disc is painful. According to another embodiment, theMRS diagnostic processor is configured diagnose the disc with respect to% probability the disc is not painful. According to one variation of thepreceding embodiments, the MRS diagnostic processor is configured todiagnose the disc with respect to % probability the disc is painful ornot painful based upon a calculated value for the disc using acquiredMRS spectral information for the disc against an empirical prior testdata set of similarly calculated values for other sample discscorrelated with % predictive values against known or assumedclassifications for such other sample discs as painful vs. non-painful.According to another embodiment, the MRS diagnostic processor isconfigured to assign a value for the disc that is referenced against arange for use in determining presence, absence, or level of pain.According to another embodiment, the MRS diagnostic processor isconfigured to provide the diagnostically useful information in a displayprovided contextually with an MRI image of the respective lumbar spinecomprising the disc. According to another embodiment, the MRS diagnosticprocessor is configured to provide the diagnostically useful informationin a display overlay onto an MRI image of the respective lumbar spinecomprising the disc. According to one variation of this embodiment, thedisplay overlay associates the diagnostically useful information withone or more intervertebral discs evaluated. According to anothervariation, the display overlay comprises a scaled legend of values alonga range, and an indicator of a result referenced against the range inthe legend and associated with an intervertebral disc evaluated.According to another variation, the display overlay comprises both colorcoding and numerical coding of results in a legend and for at least oneindicator of processed information associated with at least oneintervertebral disc evaluated by the diagnostic processor. According toanother embodiment, the MRS diagnostic processor comprises a diagnosticalgorithm empirically created by comparing acquired and processed MRSspectra for multiple intervertebral discs against control measures forpain, and that is configured to determine whether discs evaluated withthe MRS spectra are painful or non-painful. According to one variation,the diagnostic algorithm comprises at least one factor related tospectral information extracted from MRS spectral regions associated withat least one of proteoglycan, lactate, and alanine chemicals. Accordingto one applicable feature of this variation, the spectral information isextracted from an MRS spectral region associated with n-acetyl resonanceassociated with proteoglycan. According to one feature of thisvariation, the extracted information related to at least one said regionis adjusted according to an adjustment factor related to voxel volume.According to one example, the extracted information related to at leastone said region is divided by voxel volume. According to another featureof this variation, the extracted information related to at least onesaid region is adjusted according to an adjustment factor related tobody mass index (BMI). According to one example, the extractedinformation related to at least one said region is multiplied by bodymass index (BMI) of the patient. According to another example, theextracted information is multiplied by BMI of the patient divided by areference BMI. According to a further example, the reference BMI isaverage BMI calculated across an empirical test data set from which thediagnostic algorithm is statistically developed for correlation to theclassifications. According to another feature of this variation, theextracted information related to at least one said region comprises apeak value in the region. According to another feature of thisvariation, the extracted information related to at least one said regioncomprises a power value in the region. According to another applicablefeature, the diagnostic algorithm comprises at least two factors relatedto spectral information extracted from the MRS spectral regionsassociated with at least two of said chemicals. According to anotherapplicable feature, the diagnostic algorithm comprises three factorsrelated to spectral information extracted from the MRS spectral regionsassociated with all three of said chemicals. According to oneparticularly beneficial example of this feature, each of the threefactors is related to one of the proteoglycan, lactate, and alaninechemicals, respectively. According to another applicable feature, thediagnostic algorithm comprises at least two factors related to spectralinformation extracted from MRS spectral regions associated with at leastthree said chemicals. According to one particularly beneficial exampleof this feature, a first factor is related to spectral informationextracted from the MRS spectral region associated with proteoglycan(e.g. n-acetyl peak region), and a second factor is related to spectralinformation extracted from MRS spectral regions associated with lactateand alanine in combination. According to another particularly beneficialfeature, the diagnostic algorithm comprises a factor related to spectralinformation extracted from MRS spectral regions associated with each oflactate and alanine chemicals in combination. According to one highlybeneficial example of this feature, the factor comprises maximum peakvalue across the combination of the lactate and alanine spectralregions. According to another highly beneficial example, the factorcomprises a power value across the combination of the lactate andalanine spectral regions. According to another applicable feature, thediagnostic algorithm comprises at least two said factors related tospectral information extracted from the MRS spectral regions associatedwith all three of said chemicals. According to still another applicablefeature, at least one said factor is weighted by a constant. Accordingto another applicable feature, at least one said factor comprises aratio of at least two values associated with information extracted fromthe MRS spectra at regions associated with at least two of proteoglycan,lactate, and alanine chemicals. According to still a further variation,the algorithm comprises four factors associated with MRS spectral dataassociated with proteoglycan region, lactate region,proteoglycan:lactate region ratio, and proteoglycan:alanine regionratio. According to one applicable feature of this variation, thealgorithm comprises four factors associated with MRS spectral dataassociated with proteoglycan region divided by voxel volume, lactateregion divided by voxel volume, proteoglycan:lactate region ratio, andproteoglycan:alanine region ratio. According to still another applicablefeature, the four factors are weighted by constants. According to stilla further variation, the algorithm is configured to calculate adiagnostically useful value based upon PG/LA, PG/AL, PG/vol, and LA/volfactors, wherein PG=peak measurement in proteoglycan spectral region,AL=peak measurement in alanine region, LA=peak measurement in LA region,and vol=volume of prescribed voxel in the disc used for MRS dataacquisition. According to still a further variation, the algorithm isconfigured to calculate a diagnostically useful value as follows:Value=−[log(PG/LA*(0.6390061)+PG/AL*(1.45108778)+PG/vol*(1.34213514)+LA/VOL*(−0.5945179)−2.8750366)];wherein PG=peak measurement in proteoglycan spectral region, AL=peakmeasurement in alanine region, LA=peak measurement in LA region, andvol=volume of prescribed voxel in disc used for MRS data acquisition.Further to this algorithm, however, it is to be appreciated that, thoughconsidered highly beneficial, the specific constants may be slightlyvaried, and aspects such as the negative and log multipliers of theoverall remaining functions may not be absolutely necessary and theremoval of these aspects may still provide sufficiently robust results(e.g. the negative multiplier inverts negative values, otherwisecorresponding with painful results to positive numbers as morecolloquially corresponding with “positive” test results indicating paincondition is present, and visa versa for negative test results; and thelog function provides collapse of data distribution spread not necessaryfor all applications and not necessarily altering ultimate results).According to still a further applicable feature, the calculateddiagnostically useful value is compared against a threshold value ofzero (0) to determine pain diagnosis. According to still a furtherapplicable feature, positive calculated values are considered painfuland negative calculated values are considered non-painful diagnoses.According to another variation, the diagnostic algorithm is based atleast in part upon a feature associated with a combined spectral regionassociated with lactate and alanine chemicals. According to anothervariation, the diagnostic algorithm is based at least in part upon apower measurement taken along an MRS spectral region that combinesregions associated with lactate and alanine chemicals.

According to another mode of the MRS diagnostic processor aspects of thedisclosure, the diagnostic processor is provided in combination with oneor more of the various other aspects, modes, embodiments, variations,and features related to other MRS pulse sequence and/or MRS signalprocessor disclosures also provided herein.

According to another mode of the present aspect, the MRS diagnosticprocessor may be configured to implement the following equation:Score=−4.6010405+1.58785166(BLA)−0.081991(VBLAAL−29.3125)*(VBLAAL−29.3125)+0.01483355(PG/MAXLAAL−7.14499)*(PG/MAXLAAL−7.14499)*(PG/MAXLAAL−7.14499)+0.1442603(MAXLAAL/vol−16.1202)*(VBLAAL−29.3125)−0.0008879(VBLAAL−29.3125)²*(MAXLAAL/VOL−16.1202)where BLA is the BMI corrected LA spectral peak, VBLAAL is the ROIvolume and BMI normalized sum of the LA and AL spectral peaks, MAXLAALis the maximum of either the LA or AL peaks, and PG is the n-acetylspectral peak.

According to another mode of the present aspect, the MRS diagnosticprocessor may be configured to implement one or more of the followingequations:

High Lipid ClassifierScore=−(−335.51971+0.00010632*(LAVVBMI)²+873.744714*(PG/(LAALVVBMI)));where LAVVBMI equals the voxel volume and BMI adjusted LA peak value.

PG/MAXLAAL>1.85, Non-lipid, ClassifierScore=−(−1.4959544+1.72223147*(PG(MAXLAAL)));where PG/MAXLAAL equals the PG peak value divided by the maximum peakvalue of the LAAL region.

PG/MAXLAAL<1.85, Non-lipid, ClassifierScore=−1*(−134.40909800961+3.96992556918043*LAVVBMI−2.6198628365642*ALVVBMI+113.683315467568*ALAUCVVBMI−149.65896624348*SQRT(PGAUCVVBMI));where LAVVBMI is the voxel volume and BMI adjusted LA peak value,ALVVBMI is the voxel volume and BMI adjusted AL peak value, ALAUCVVBMIis the AL region area under the curve as voxel volume and BMI adjusted,and PGAUCVVBMI is the PG region area under the curve as voxel volume andBMI adjusted.

It is to be appreciated that these formulaic relationships shown above,and elsewhere herein, are examples of highly accurate results that havebeen enjoyed with the present embodiments when put into practice.However, the examples are also provided in fine detail for fulldisclosure and understanding. These finer details are not intended to benecessarily limiting in all cases. For example, many of the constantsdisclosed herein are shown to many decimal points, which is the formatgenerated by the engineering platforms employed to generate them. Itwould be readily apparent to one of ordinary skill that these likelycould be significantly truncated or rounded without significantdegradation or departing from the scope of the present disclosure. Inaddition, in order to provide abundance of understanding and disclosure,certain theories and explanations may be put forth and postulatedherein, which may not be fully accurate, and are not necessary in orderto fully embrace and enjoy the present embodiments and invention. Thenovelty and beneficial utility of the present embodiments may be fullyappreciated and enjoyed without being bound by theory, as should beappreciated by one of ordinary skill.

Another aspect of the present disclosure comprises a diagnostic systemconfigured to generate information useful for diagnosing a medicalcondition or chemical environment in a tissue of a subject based atleast in part upon a combination of lactate-related and alanine-relatedfactors measured or estimated in the tissue. According to one mode ofthis aspect, the diagnostic system is configured to generate the usefulinformation based at least in part upon one combination lactate-alanine(LAAL)-related diagnostic factor related to a combination oflactate-related and alanine-related factors measured or estimated in thetissue. According to one embodiment of this mode, the combination LAALfactor provides useful information as a LAAL biomarker for hypoxia inthe tissue. According to another embodiment of this mode, the diagnosticsystem is further configured to provide the useful information based onthe LAAL factor in combination with a second factor related to a thirdchemical-related factor measured or estimated in the tissue. Accordingto one variation of this embodiment, the third chemical-related factorcomprises a biomarker associated with enervation of the tissue.According to another variation of this embodiment, the thirdchemical-related factor is associated with proteoglycan content in thetissue. According to another mode of this aspect, the diagnostic systemcomprises an MRS diagnostic processor, and the lactate-related andalanine-related factors comprise features associated withlactate-related and alanine-related regions of an MRS spectrum of aregion of interest in the tissue. According to one embodiment of thismode, the MRS diagnostic processor is further configured to generate theuseful information based at least in part upon one combinationlactate-alanine (LAAL)-related diagnostic factor related to acombination of the lactate-related and alanine-related factors measuredor estimated in the tissue. According to one variation of thisembodiment, the combination LAAL factor comprises a maximum peakspectral value in the combined LAAL region of the MRS spectrum.According to another variation of this embodiment, the combination LAALfactor comprises a measured or estimated overall power value in thecombined LAAL region of the MRS spectrum.

Another aspect of the present disclosure is an MRS system comprising anMRS pulse sequence, MRS signal processor, and MRS diagnostic processor,and which is configured to generate, acquire, and process an MRSspectrum for providing diagnostically useful information associated witha region of interest in a body of a patient. According to one mode ofthis aspect, the MRS system comprising the MRS pulse sequence, MRSsignal processor, and MRS diagnostic processor, is configured togenerate, acquire, and process the MRS spectrum for a voxel principallylocated in an intervertebral disc in the body of the patient and toprovide diagnostically useful information associated with the disc.According to one embodiment of this mode, the voxel is principallylocated in a nucleus of the disc. According to another embodiment ofthis mode, the diagnostically useful information is useful fordiagnosing pain or absence of pain associated with the disc. Variousfurther modes of this aspect are contemplated that comprise one or moreof the various aspects, modes, embodiments, variations, and features ofthe MRS pulse sequence, MRS signal processor, and MRS diagnosticprocessor as elsewhere described herein. According to one such furthermode, for example, the MRS pulse sequence comprises a combinationCHESS-PRESS sequence. According to another example of such a mode, theMRS pulse sequence comprises a combination CHESS-VSS-PRESS sequence.According to another such further mode, the MRS pulse sequence comprisesa TE of about 28 ms and a TR of about 1000 ms, whereas TE according tofurther embodiments can range from between about 25 to about 40 ms andTR can typically range from between about 750 to about 2000 ms.According to another such further mode, the MRS signal processorcomprises at least one of a channel selector, a phase shift corrector,an apodizer, a frame editor, a frequency shift corrector, and a frameaveraging combiner. According to another mode, the MRS diagnosticprocessor is configured to calculate and provide diagnostically usefulinformation for diagnosing pain associated with at least oneintervertebral disc based upon at least one MRS spectral regionassociated with at least one of proteoglycan, lactate, and alaninechemicals. According to one embodiment of this mode, informationassociated with each of the MRS spectral regions associated with each ofthese chemicals is used by the MRS diagnostic processor in providing thediagnostically useful information. According to another embodiment, acombination LAAL factor associated with a combination of thelactate-related and alanine-related MRS spectral regions is used.According to one variation of this embodiment, the combination LAALfactor is used in further combination with a second factor associatedwith a proteoglycan-related (such as for example n-acetyl) MRS spectralregion for an overall diagnostic algorithm.

According to another mode of the various aspects above, each or all ofthe respective MRS system components described is provided as user orcontroller operable software in a non-transitory computer readablestorage medium configured to be installed and operated by one or moreprocessors. According to one embodiment of this mode, a non-transitorycomputer operable storage medium is provided and stores the operablesoftware.

The following issued US patents are also herein incorporated in theirentirety by reference thereto: U.S. Pat. Nos. 5,617,861; 5,903,149;6,617,169; 6,835,572; 6,836,114; 6,943,033; 7,042,214; 7,319,784.

The following pending US Patent Application Publication is hereinincorporated in its entirety by reference thereto: US2007/0253910.

The following PCT Patent Application Publication is also hereinincorporated in its entirety by reference thereto: WO2009/058915.

Some aspects of the systems and methods described herein canadvantageously be implemented using, for example, computer software,hardware, firmware, or any combination of computer software, hardware,and firmware. Computer software can comprise computer executable codestored in a computer readable medium that, when executed, performs thefunctions described herein. In some embodiments, computer-executablecode is executed by one or more general purpose computer processors. Askilled artisan will appreciate, in light of this disclosure, that anyfeature or function that can be implemented using software to beexecuted on a general purpose computer can also be implemented using adifferent combination of hardware, software, or firmware. For example,such a module can be implemented completely in hardware using acombination of integrated circuits. Alternatively or additionally, sucha feature or function can be implemented completely or partially usingspecialized computers designed to perform the particular functionsdescribed herein rather than by general purpose computers.

A skilled artisan will also appreciate, in light of this disclosure,that multiple distributed computing devices can be substituted for anyone computing device illustrated herein. In such distributedembodiments, the functions of the one computing device are distributed(e.g., over a network) such that some functions are performed on each ofthe distributed computing devices.

Some embodiments of the present invention may be described withreference to equations, algorithms, and/or flowchart illustrations ofmethods according to embodiments of the invention. These methods may beimplemented using computer program instructions executable on one ormore computers. These methods may also be implemented as computerprogram products either separately, or as a component of an apparatus orsystem. In this regard, each equation, algorithm, or block or step of aflowchart, and combinations thereof, may be implemented by hardware,firmware, and/or software including one or more computer programinstructions embodied in computer-readable program code logic. As willbe appreciated, any such computer program instructions may be loadedonto one or more computers, including without limitation a generalpurpose computer or special purpose computer, or other programmableprocessing apparatus to produce a machine, such that the computerprogram instructions which execute on the computer(s) or otherprogrammable processing device(s) implement the functions specified inthe equations, algorithms, and/or flowcharts. It will also be understoodthat each equation, algorithm, and/or block in flowchart illustrations,and combinations thereof, may be implemented by special purposehardware-based computer systems which perform the specified functions orsteps, or combinations of special purpose hardware and computer-readableprogram code logic means.

Furthermore, computer program instructions, such as embodied incomputer-readable program code logic, may also be stored in a computerreadable memory (e.g., a non-transitory computer readable medium) thatcan direct one or more computers or other programmable processingdevices to function in a particular manner, such that the instructionsstored in the computer-readable memory implement the function(s)specified in the block(s) of the flowchart(s). The computer programinstructions may also be loaded onto one or more computers or otherprogrammable computing devices to cause a series of operational steps tobe performed on the one or more computers or other programmablecomputing devices to produce a computer-implemented process such thatthe instructions which execute on the computer or other programmableprocessing apparatus provide steps for implementing the functionsspecified in the equation(s), algorithm(s), and/or block(s) of theflowchart(s).

While various alternative modalities may be employed as stated, oneparticular example of an overall diagnostic system 200 and variousrelated functional interfacing components are shown in FIGS. 41A-B andreferenced with respect to schematic flow of an exam and related stepspost-DDD-MRS pulse sequence acquisition as follows. FIG. 41A shows aDDD-MRS pulse sequence acquisition and output communication flowdiagram. An MRI exam is first conducted on the patient 202 who typicallyis slid supine into MR system 210 while lying on a spine detector coil220 that acquires the MR and MRS signals. This is followed by theDDD-MRS pulse sequence, as also conducted via the same MR system 210 andby a trained operator/technician 204. Data representative of the anatomyof the patient 202 is generated 258 (e.g., data representative of thechemical makeup of an area of interest inside the intervertebral disc ofthe patient's spine 252). The results are then packaged in a dataarchive folder 250 that includes information related to the MRI image258 (if taken and retained for this purpose), voxel prescription invarious relevant planes 252, pre-packaged output spectra 254 (if desiredfor any further use, or not), complex data files for the acquired series256. This is sent via PACS 260 for storage and/or further communicationeither as push or pull for further processing. In some embodiments, theMR system 210 may be operated by a computer system or terminal 230 thatcan be located remotely or can be integrated into the MR system 210, toallow one or more operators 240 (or the technician 204) to provideinstructions or other information to the MR system 210.

As shown in FIG. 41B, this data package 250 may then be accessed orpushed from the PACS 260 to another local DDD-MRS engine 261, which maybe a local computer 262 (and related peripheral devices such as display264 and keyboard 266), work station, or other modality, or terminal(e.g., terminal 230), which may conduct the DDD-MRS signal processingand/or diagnostic processing and for packaged display of results asappropriate. This may be monitored via other remote device 290, such asvia the internet 270 as shown schematically in FIG. 41B—and this mayinclude for example license monitoring such as on a “per click” or“volume”-related use license fee basis or other such use monitoringpurposes (e.g. data collection and analysis purposes, e.g. for trials,studies, registries, etc.). The more remote processors may be a centralserver 292 providing certain SAAS support to the system, or again formore monitoring. These files, at any stage, can be configured to push orbe pulled electronically, such as to a remote DDD-MRS station 280 withengine components including a computer 282, monitor 284, keyboard 286,where diagnostic results such as overlaid images 288 may be seen andanalyzed for example and the various processors may be stored andemployed for functional use in a variety of single or multiplecoordinated locations and controllers or computers, with ultimateflexibility re: specific modality for operation and storage 294 and/orcommunication of results.

While certain embodiments of the disclosure have been described, theseembodiments have been presented by way of example only, and are notintended to limit the scope of the broader aspects of the disclosure.Indeed, the novel methods, systems, and devices described herein may beembodied in a variety of other forms. For example, embodiments of oneillustrated or described DDD-MRS system component may be combined withembodiments of another illustrated or described DDD-MRS systemcomponent. Moreover, the DDD-MRS system components described above, e.g.pulse sequence, signal processor, or diagnostic processor, may beutilized for other purposes. For example, an MRS system (or componentsequence, signal processor, or diagnostic processor useful therewith ortherein), may be configured and used in manners consistent with one ormore broad aspects of this disclosure for diagnosing other tissueenvironments or conditions than pain within an intervertebral disc. Or,such may be usefully employed for diagnosing pain or other tissueenvironments or conditions in other regions of interest within the body.Such further applications are considered within the broad scope ofdisclosure contemplated herein, with or without further modifications,omissions, or additions that may be made by one of ordinary skill for aparticular purpose. Furthermore, various omissions, substitutions andchanges in the form of the methods, systems, and devices describedherein may be made without departing from the spirit of the disclosure.Components and elements may be altered, added, removed, or rearranged.Additionally, processing steps may be altered, added, removed, orreordered. While certain embodiments have been explicitly described,other embodiments will also be apparent to those of ordinary skill inthe art based on this disclosure.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a magneticresonance spectroscopy (MRS) processing system configured to process arepetitive frame MRS spectral acquisition series generated and acquiredfor a voxel principally located within an intervertebral disc via an MRSpulse sequence, and acquired at multiple parallel acquisition channelsof a multi-coil spine detector assembly, in order to provide diagnosticinformation associated with the disc, comprising: an MRS signalprocessor comprising a channel selector, a phase shift corrector, afrequency shift corrector, a frame editor, and a channel combiner, andconfigured to receive and process the MRS spectral acquisition seriesfor the disc and to generate a processed MRS spectrum for the serieswith sufficient signal-to-noise ratio (SNR) to acquire informationassociated with identifiable features along MRS spectral regionsassociated with unique chemical constituents in the disc; and an MRSdiagnostic processor configured to extract data from identifiablechemical regions in the processed MRS spectrum in a manner that providesdiagnostic information for diagnosing a medical condition or chemicalenvironment associated with the disc.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a magneticresonance spectroscopy (MRS) processing system configured to process arepetitive frame MRS spectral acquisition series generated and acquiredfor a voxel principally located within an intervertebral disc via an MRSpulse sequence, and acquired at multiple parallel acquisition channelsof a multi-coil spine detector assembly, in order to provide diagnosticinformation associated with the disc, comprising: an MRS signalprocessor comprising a channel selector, a phase shift corrector, afrequency shift corrector, a frame editor, and a channel combiner, andconfigured to receive and process the MRS spectral acquisition seriesfor the disc and to generate a processed MRS spectrum for the serieswith sufficient signal-to-noise ratio (SNR) to acquire informationassociated with identifiable features along MRS spectral regionsassociated with unique chemical constituents in the disc; and an MRSdiagnostic processor configured to extract data from identifiablechemical regions in the processed MRS spectrum in a manner that providesdiagnostic information for diagnosing a medical condition or chemicalenvironment associated with the disc.

In one embodiment, a magnetic resonance spectroscopy (MRS) processingmethod is used for processing a repetitive frame MRS spectralacquisition series generated and acquired for a voxel principallylocated within an intervertebral disc via an MRS pulse sequence, andacquired at multiple parallel acquisition channels of a multi-coil spinedetector assembly, and for providing diagnostic information associatedwith the disc, the method comprising: receiving the MRS spectralacquisition series from the multiple acquisition channels; signalprocessing the MRS acquisition series, comprising selecting one or morechannels among the parallel channels based upon a predeterminedcriteria, recognizing and correcting phase shift error among multipleframes within the series of a channel acquisition, recognizing andcorrecting a frequency shift error between multiple frames within theseries of the channel acquisition, recognizing and editing out framesfrom the series based upon a predetermined criteria, combining selectedand corrected channels for a combined average processed MRS spectrum;and diagnostically processing the processed MRS spectrum by extractingdata from identifiable chemical regions in the processed MRS spectrumand processing the extracted data in a manner that provides MRS-baseddiagnostic information for diagnosing a medical condition or chemicalenvironment associated with the disc.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a signal processor configured to signalprocess a repetitive multi-frame MRS pulse sequence acquisition seriesof MRS spectra frames received from multiple acquisition channels of adetector assembly during a MRS pulse sequence series conducted on aregion of interest (ROI) within a tissue in a body of a subject; andwherein the signal processor comprises a channel selector configured tomeasure a parameter related to MRS spectral signal quality for theacquired MRS spectral series from each acquisition channel, compare themeasured parameters for the respective channels against at least onethreshold criteria for channel selection, identify a number of selectedchannels which meet or exceed the threshold criteria and a number ofother failed channels which fail to meet the threshold criteria, andretain the selected channels and discard the failed channels from theacquisition series.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a signal processor configured to signalprocess a repetitive multi-frame MRS pulse sequence acquisition seriesof MRS spectra frames received from multiple acquisition channels of adetector assembly during a MRS pulse sequence series conducted on aregion of interest (ROI) within a tissue in a body of a subject; andwherein the signal processor comprises a channel selector configured tomeasure a parameter related to MRS spectral signal quality for theacquired MRS spectral series from each acquisition channel, compare themeasured parameters for the respective channels against at least onethreshold criteria for channel selection, identify a number of selectedchannels which meet or exceed the threshold criteria and a number ofother failed channels which fail to meet the threshold criteria, andretain the selected channels and discard the failed channels from theacquisition series.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a signal processor configured to processa repetitive multi-frame MRS pulse sequence acquisition series of MRSspectra frames received from an acquisition channel of a detectorassembly during a MRS pulse sequence series conducted on a region ofinterest (ROI) within a tissue in a body of a subject; wherein thesignal processor comprises a frame editor configured to measure aparameter related to signal quality for the MRS spectrum for eachacquired frame of the acquisition series, compare the measured valuesfor the parameter for the respective frames against a thresholdcriteria, and designate a number of successful frames that meet thethreshold criteria and a number of failed frames that fail to meet thethreshold criteria; and wherein the frame editor is further configuredto retain successful frames in the acquisition series, and edit out thefailed frames from the acquisition series if number of successful framesmeets or exceeds a minimum frame number threshold, but to retain atleast some of the failed frames in the acquisition series if the numberof successful frames is below the minimum frame number threshold.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a signal processor configured to processa repetitive multi-frame MRS pulse sequence acquisition series of MRSspectra frames received from an acquisition channel of a detectorassembly during a MRS pulse sequence series conducted on a region ofinterest (ROI) within a tissue in a body of a subject; wherein thesignal processor comprises a frame editor configured to measure aparameter related to signal quality for the MRS spectrum for eachacquired frame of the acquisition series, compare the measured valuesfor the parameter for the respective frames against a thresholdcriteria, and designate a number of successful frames that meet thethreshold criteria and a number of failed frames that fail to meet thethreshold criteria; and wherein the frame editor is further configuredto retain successful frames in the acquisition series, and edit out thefailed frames from the acquisition series if number of successful framesmeets or exceeds a minimum frame number threshold, but to retain atleast some of the failed frames in the acquisition series if the numberof successful frames is below the minimum frame number threshold.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a signal processor configured to processa repetitive multi-frame MRS pulse sequence acquisition series of MRSspectra frames received from an acquisition channel of a detectorassembly during a MRS pulse sequence series conducted on a region ofinterest (ROI) within a tissue in a body of a subject; wherein thesignal processor comprises a frequency error corrector configured tocalculate a confidence level in an ability to estimate frequency shifterror for the MRS spectra of each frame of the series, compare eachcalculated confidence level for each frame against at least onethreshold criteria, and determine a number of successful frames thatmeet or exceed the threshold criteria and a number of other failedframes that fail to meet the threshold criteria; and wherein the signalprocessor is further configured to automatically determine whether to(a) edit out the failed frames from the acquisition series and performfrequency shift error correction via the frequency error corrector in amanner to at least in part reverse the frequency shift error estimate oneach of the successful frames, if the number of successful frames meetsor exceeds a minimum threshold number, or (b) retain at least some ofthe failed frames and not perform frequency error correction to theseries via the frequency error corrector if the number of successfulframes is below the minimum threshold.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a signal processor configured to processa repetitive multi-frame MRS pulse sequence acquisition series of MRSspectra frames received from an acquisition channel of a detectorassembly during a MRS pulse sequence series conducted on a region ofinterest (ROI) within a tissue in a body of a subject; wherein thesignal processor comprises a frequency error corrector configured tocalculate a confidence level in an ability to estimate frequency shifterror for the MRS spectra of each frame of the series, compare eachcalculated confidence level for each frame against at least onethreshold criteria, and determine a number of successful frames thatmeet or exceed the threshold criteria and a number of other failedframes that fail to meet the threshold criteria; and wherein the signalprocessor is further configured to automatically determine whether to(a) edit out the failed frames from the acquisition series and performfrequency shift error correction via the frequency error corrector in amanner to at least in part reverse the frequency shift error estimate oneach of the successful frames, if the number of successful frames meetsor exceeds a minimum threshold number, or (b) retain at least some ofthe failed frames and not perform frequency error correction to theseries via the frequency error corrector if the number of successfulframes is below the minimum threshold.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a signal quality evaluator configured toautomatically determine whether or not an MRS spectrum acquired from aregion of interest (ROI) in a tissue in a body of a subject via an MRSpulse sequence series exam of the ROI comprises a regional signaturesignal along the MRS spectrum that is characteristic of lipid.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a signal quality evaluator configured toautomatically determine whether or not an MRS spectrum acquired from aregion of interest (ROI) in a tissue in a body of a subject via an MRSpulse sequence series exam of the ROI comprises a regional signaturesignal along the MRS spectrum that is characteristic of lipid.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) in atissue in a body of a subject based at least in part upon at least onechemical factor related to information extracted from the ROI andassociated with lactate (LA) and alanine (AL) chemicals.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) in atissue in a body of a subject based at least in part upon at least onechemical factor related to information extracted from the ROI andassociated with lactate (LA) and alanine (AL) chemicals.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) in atissue in a body of a subject based at least in part upon a chemicalfactor related to information extracted from the ROI and associated witha chemical and as adjusted by an adjustment factor that comprises atleast one of a voxel-related adjustment factor associated with a voxelprescribed to correspond with the ROI and related to the informationextracted, and a subject-dependent variable-related adjustment factorassociated with the subject.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) in atissue in a body of a subject based at least in part upon a chemicalfactor related to information extracted from the ROI and associated witha chemical and as adjusted by an adjustment factor that comprises atleast one of a voxel-related adjustment factor associated with a voxelprescribed to correspond with the ROI and related to the informationextracted, and a subject-dependent variable-related adjustment factorassociated with the subject.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic processed information for diagnosing a medicalcondition or chemical environment associated with a region of interest(ROI) in a tissue in a body of a subject based at least in part upontaking a first MRS measurement for a chemical factor taken at a regionof an MRS spectrum acquired from the ROI and associated with a chemicaland comparing the first MRS measurement against a value derived from adifferent second measurement and that is associated with an amount ofthe chemical in the ROI.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic processed information for diagnosing a medicalcondition or chemical environment associated with a region of interest(ROI) in a tissue in a body of a subject based at least in part upontaking a first MRS measurement for a chemical factor taken at a regionof an MRS spectrum acquired from the ROI and associated with a chemicaland comparing the first MRS measurement against a value derived from adifferent second measurement and that is associated with an amount ofthe chemical in the ROI.

In one embodiment, a computing system, comprising one or moremicroprocessors receiving at least one signal responsive to datacollected in an MR scanner, is configured to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) of atissue in a body of a subject based at least in part upon a chemicalfactor related to information extracted from the ROI and associated witha chemical; and wherein said diagnostic information comprises aprobability value assigned to a likelihood that the medical condition orchemical environment meets certain criteria in the ROI.

In one embodiment, a physical computer readable medium stores computerexecutable code that causes a computing system to implement a medicaldiagnostic system, comprising: a diagnostic processor configured toprovide diagnostic information for diagnosing a medical condition orchemical environment associated with a region of interest (ROI) of atissue in a body of a subject based at least in part upon a chemicalfactor related to information extracted from the ROI and associated witha chemical; and wherein said diagnostic information comprises aprobability value assigned to a likelihood that the medical condition orchemical environment meets certain criteria in the ROI.

In one embodiment, a medical diagnostic method comprises: using acomputing system to implement a signal processor for signal processing arepetitive multi-frame MRS pulse sequence acquisition series of MRSspectra frames received from multiple acquisition channels of a detectorassembly during a MRS pulse sequence series conducted on a region ofinterest (ROI) within a tissue in a body of a subject; and wherein thesignal processing further comprises using one or more microprocessors tooperate a channel selector for measuring a parameter related to MRSspectral signal quality for the acquired MRS spectral series from eachacquisition channel, comparing the measured parameters for therespective channels against at least one threshold criteria for channelselection, identifying a number of selected channels which meet orexceed the threshold criteria and a number of other failed channelswhich fail to meet the threshold criteria, and retaining the selectedchannels and discarding the failed channels from the acquisition series.

In one embodiment, a medical diagnostic method comprises: using acomputing system for signal processing a repetitive multi-frame MRSpulse sequence acquisition series of MRS spectra frames received from anacquisition channel of a detector assembly during a MRS pulse sequenceseries conducted on a region of interest (ROI) within a tissue in a bodyof a subject; wherein the signal processing further comprises using acomputing system for implementing a frame editor for measuring, usingone or more microprocessors, a parameter related to signal quality forthe MRS spectrum for each acquired frame of the acquisition series,comparing, using the one or more microprocessors, the measured valuesfor the parameter for the respective frames against a thresholdcriteria, and designating, using the one or more microprocessors, anumber of successful frames that meet the threshold criteria and anumber of failed frames that fail to meet the threshold criteria; andwherein the frame editing further comprises retaining, using the one ormore microprocessors, successful frames in the acquisition series, andediting out the failed frames from the acquisition series if the numberof successful frames meets or exceeds a minimum frame number threshold,but retaining at least some of the failed frames in the acquisitionseries if the number of successful frames is below the minimum framenumber threshold.

In one embodiment, a medical diagnostic method, comprising: using acomputing system to execute executable code for implementing a signalprocessor for processing a repetitive multi-frame MRS pulse sequenceacquisition series of MRS spectra frames received from an acquisitionchannel of a detector assembly during a MRS pulse sequence seriesconducted on a region of interest (ROI) within a tissue in a body of asubject; wherein the signal processing further comprises using acomputing system to execute executable code for operating a frequencyerror corrector for calculating, using one or more microprocessors, aconfidence level in an ability to estimate frequency shift error for theMRS spectra of each frame of the series, comparing, using the one ormore microprocessors, each calculated confidence level for each frameagainst at least one threshold criteria, and determining, using the oneor more microprocessors, a number of successful frames that meet orexceed the threshold criteria and a number of other failed frames thatfail to meet the threshold criteria; and wherein the signal processingfurther comprises using a computing system to execute executable codefor automatically determining whether to (a) edit out the failed framesfrom the acquisition series and perform frequency shift error correctionvia the frequency error corrector in a manner to at least in partreverse the frequency shift error estimate on each of the successfulframes, if the number of successful frames meets or exceeds a minimumthreshold number, or (b) retaining at least some of the failed framesand not performing frequency error correction to the series via thefrequency error corrector if the number of successful frames is belowthe minimum threshold.

In one embodiment, a medical diagnostic method comprises: using acomputing system to execute executable code to implement a signalquality evaluator for automatically determining, using one or moremicroprocessors, whether or not an MRS spectrum acquired from a regionof interest (ROI) in a tissue in a body of a subject via an MRS pulsesequence series exam of the ROI comprises a regional signature signalalong the MRS spectrum that is characteristic of lipid.

In one embodiment, a medical diagnostic method comprises: using acomputing system to execute executable code to implement a diagnosticprocessor for providing, using one or more microprocessors, diagnosticinformation for diagnosing a medical condition or chemical environmentassociated with a region of interest (ROI) in a tissue in a body of asubject based at least in part upon at least one chemical factor relatedto information extracted from the ROI and associated with lactate (LA)and alanine (AL) chemicals.

In one embodiment, a medical diagnostic method, comprising: using acomputing system to execute executable code to implement a diagnosticprocessor for providing diagnostic information for diagnosing a medicalcondition or chemical environment associated with a region of interest(ROI) in a tissue in a body of a subject based at least in part upon achemical factor related to information extracted from the ROI andassociated with a chemical and comprising adjusting, using one or moremicroprocessors, the chemical factor by an adjustment factor thatcomprises at least one of a voxel-related adjustment factor associatedwith a voxel prescribed to correspond with the ROI and related to theinformation extracted, and a subject-dependent variable-relatedadjustment factor associated with the subject.

In one embodiment, a medical diagnostic method, comprising: using acomputing system to execute executable code to implement a diagnosticprocessor for providing processed diagnostic information for diagnosinga medical condition or chemical environment associated with a region ofinterest (ROI) in a tissue in a body of a subject based at least in partupon taking a first MRS measurement for a chemical factor taken at aregion of an MRS spectrum acquired from the ROI and associated with achemical, and comparing, using one or more microprocessors, the firstMRS measurement against a value derived from a different secondmeasurement and that is associated with an amount of the chemical in theROI.

In one embodiment, a medical diagnostic method comprises: using acomputing system to execute executable code to implement a diagnosticprocessor for providing diagnostic information for diagnosing a medicalcondition or chemical environment associated with a region of interest(ROI) of a tissue in a body of a subject based at least in part upon achemical factor related to information extracted from the ROI andassociated with a chemical; and using a computing system to executeexecutable code for providing, using one or more microprocessors, thediagnostic information that comprises a probability value assigned to alikelihood that the medical condition or chemical environment meetscertain criteria in the ROI.

TABLE 1 Examples of CV Variables for DDD-MRS CHESS-VSS-PRESS pulsesequence for generating MRS spectra useful for post- processing anddiagnosing DDD pain of lumbar intervertebral discs (e.g. in a 3.0 TeslaMRI system) CV Variable Value TE (usec) 28000 TR (usec) 1000000Acquisition Matrix Size 1 Acquisition Matrix Size 1 Number of spatialslices 1 Water Suppression Method 1 CHESS Flip Angle 1 1050 CHESS FlipAngle 2 800 CHESS Flip Angle 3 125 VSS Band Configuration 7 PRESSCorrection-X axis 1.2 PRESS Correction-Yaxis 1.2 PRESS Correction-Z axis1.2 Number of Frames 300 PRESS Flip Angle 1 90 PRESS Flip Angle 2 167PRESS Flip Angle 3 167 PRESS Correction Function 0

TABLE 2 Example 1, DDD-MRS Clinical Study Group Demographics andComparison DDD-MRS Clinical Study-Group Demographics Pain PatientsAsymptomatics p value By SUBJECT (n = 31) n= 12 19 Male  7 (58%)  9(47%) Female  5 (42%) 10 (53%) Age 46.6 ± 9.4 32.4 ± 11.3 ** 0.0006Height 68.3 ± 4.1 66.8 ± 4.5  0.1805 Weight 172.5 ± 38.5  151 ± 36.30.0639 BMI 25.9 ± 4.4 23.7 ± 3.99 0.0824 By DISCS (n = 52) n= 25 27 Male16 (64%) 16 (59%) Female  9 (36%) 11 (41%) Age 46.2 ± 9.04 35.2 ± 14.6** 0.0010 Height 68.7 ± 4.03 67.9 ± 4.5  0.2584 Weight 177.4 ± 39.3 157.6 ± 39.5  * 0.0381 BMI 26.2 ± 4.4  23.8 ± 4.3  * 0.0280 Pos.Controls Neg. Controls p value By DISCS (n = 52) n= 13 39 Male 8 (62%)24 (62%) Female 5 (38%) 15 (38%) Age   46 ± 9.7 38.7 ± 13.9 * 0.0445Height 68.9 ± 3.7 68.1 ± 4.4  0.2661 Weight 182.4 ± 35.9  162 ± 40.80.0570 BMI 26.9 ± 4.2 24.4 ± 4.5  * 0.0402

TABLE 3 Example 1, Comparison of Clinical DDD-MRS Results (MRS+/−) vs.Positive and Negative Controls, per Disc DDD-MRS Results DDD-MRS ResultsPresumed TRUE Presumed FALSE % Match 3T Pain (All Disco) 23 2 92.0% 3TPos Control (Pain, PD+) 12 1 92.3% 3T Neg Control (Pain, PD−) 11 1 91.7%3T Neg Control (Asymptomatic) 27 0 100.0%  3T Neg Control 38 1 97.4%(All, PD− + Asymptomatics) 3T All 50 2 96.2%

TABLE 4 Example 1, Comparison of Clinical DDD-MRS Results (MRS+/−) vs.Positive and Negative Controls, per Conventional Diagnostic PerformanceMeasures: Sensitivity, Specificity, Positive Predictive Value (PPV),Negative Predictive Value (NPV), Global Performance Accuracy (GPA).DDD-MRS Diagnostic Performance Sensitivity 92.3% Specificity 97.4% PPV92.3% NPV 97.4% GPA 96.2%

TABLE 5 Example 2, Clinical Data Set (retrospective and prospectivecombined) By Subject (pain) (volunteer) mean ± St. Dev. mean ± St. Dev.p value Age (yrs) 45.7 ± 8.9   36 ± 12.9 p = 0.0005 Height (in) 67.8 ±4   67.2 ± 4.4 p = 0.251  Weight (lbs) 166.4 ± 39.1 154.3 ± 32.7 p =0.126  BMI 25.2 ± 4.4 23.9 ± 3.5 p = 0.147  n= 14 28 Male 7 14 Female 714 By Disc (per Subject Group) (pain) (volunteer) mean ± St. Dev. mean ±St. Dev. p value Age (yrs) 45.9 ± 8.8  35.2 ± 14.6 p = 0.001 Height (in)68.1 ± 3.92  68 ± 4.4 p = 0.358 Weight (lbs) 170 ± 40  160.7 ± 32.1  p =0.087 BMI 25.5 ± 4.4  24.3 ± 3.4  p = 0.063 n= 30 49 Male 16 28 Female14 21 By Disc (per +/− Control Group) (+control) (−control) mean ± St.Dev. mean ± St. Dev. p value Age (yrs) 45.3 ± 9.2 41.7 ± 13.2 p = 0.05 Height (in) 68.4 ± 3.7  68 ± 4.3 p = 0.398 Weight (lbs) 175.4 ± 38.5161.6 ± 34.4  p = 0.138 BMI 26.2 ± 4.4 24.4 ± 3.7  p = 0.093 n= 15 64Male 8 36 Female 7 28

TABLE 6 DDD-MRS Disc Phantom: Expected vs. Measured (Example 4) Phantom/PG Concentration (mM) LA Concentration (mM) PG/LA Ratio Disc ExpectedMeasured % Diff Expected Measured % Diff Expected Measured % Diff C/1 77.7 9% 7 7.0  0% 1 1.09 9% C/2 14 12.4 −11%  14 11.9 −15%  1 1.04 4% C/321 21.9 4% 21 25.4 21% 1 0.86 −14%  B/1 28 30.3 8% 28 29.4  5% 1 1.03 3%B/2 42 57.9 38%  14 16.4 17% 3 3.54 18%  B/3 14 14.6 4% 42 51.4 22% 0.330.28 −14%  B/4 28 23.9 −14%  28 25.0 −11%  1 0.96 −4%  B/5 42 34.3 −18% 14 11.2 −20%  3 3.07 2%

What is claimed is:
 1. A magnetic resonance spectroscopy (MRS)processing system comprising: one or more computer processors; and oneor more non-transitory computer readable media comprisingcomputer-readable instructions configured to cause the one or morecomputer processors to: receive an MRS spectral acquisition series thatincludes a set of free induction decay (FID) frames generated andacquired from a voxel located within a region of interest (ROI) via anMRS pulse sequence operation of an MRS system using multiple acquisitionchannels; determine a parameter indicative of MRS spectral quality forat least a portion of the set of FID frames of the MRS spectralacquisition series from each of the multiple acquisition channels;compare the determined parameters from the multiple acquisitionchannels; determine one or more selected channels from the multipleacquisition channels for further processing based at least in part onthe comparison of the determined parameters; and process one or moreframes of the MRS spectral acquisition series from the one or moreselected channels to generate a processed MRS spectrum.
 2. The MRSprocessing system of claim 1, wherein the computer-readable instructionsare configured to cause the one or more computer processors to: identifya spectral feature in spectra for one or more of the frames of the MRSspectral acquisition series; shift the spectra of one or more of theframes of the MRS spectral acquisition series based at least in part onthe identified spectral feature to align the identified spectral featureacross the frames; and process at least the shifted frames of the MRSspectral acquisition series from the one or more selected channels togenerate the processed MRS spectrum.
 3. The MRS processing system ofclaim 2, wherein the computer-readable instructions are configured tocause the one or more computer processors to: determine a set ofexcluded frames of the MRS spectral acquisition series that fail tosatisfy a frame editing criterion; determine a set of retained frames ofthe MRS spectral acquisition series that do satisfy the frame editingcriterion; and process the set of retrained frames of the MRS spectralacquisition series from the one or more selected channels to generatethe processed MRS spectrum.
 4. The MRS processing system of claim 1,wherein the computer-readable instructions are configured to cause theone or more computer processors to determine a strongest single channelbased at least in part upon the comparison of the determined parameters.5. The MRS processing system of claim 4, wherein the computer-readableinstructions are configured to cause the one or more computer processorsto determine the one or more selected channels to include the strongestsingle channel and one or more additional channels that are within athreshold of the determined parameter of the strongest single channel.6. The MRS processing system of claim 1, wherein the computer-readableinstructions are configured to: cause the one or more computerprocessors to produce a channel spectrum for each of the multipleacquisition channels by combining multiple frames from the respectivemultiple acquisition channels; and determine the parameter for each ofthe multiple acquisition channels from the respective channel spectra.7. The MRS processing system of claim 1, wherein the determinedparameter is a signal-to-noise ratio.
 8. The MRS processing system ofclaim 1, wherein the computer-readable instructions are configured tocause the one or more computer processors to determine the parameterusing a water signal.
 9. The MRS processing system of claim 1, whereinthe ROI includes at least a portion of an intervertebral disc of asubject.
 10. A magnetic resonance spectroscopy (MRS) processing systemcomprising: one or more computer processors; and one or morenon-transitory computer readable media comprising computer-readableinstructions configured to cause the one or more computer processors to:receive an MRS spectral acquisition series that includes a set of freeinduction decay (FID) frames generated and acquired from a voxel locatedwithin a region of interest (ROI) via an MRS pulse sequence operation ofan MRS system; determine a set of excluded frames of the MRS spectralacquisition series that fail to satisfy a frame editing criterion;determine a set of retained frames of the MRS spectral acquisitionseries that do satisfy the frame editing criterion; and process the setof retrained frames of the MRS spectral acquisition series to generate aprocessed MRS spectrum.
 11. The MRS processing system of claim 10,wherein the computer-readable instructions are configured to cause theone or more computer processors to: determine a parameter indicative ofMRS spectral quality for at least a portion of the MRS spectralacquisition series from each of multiple acquisition channels; comparethe determined parameters from the multiple acquisition channels;determine one or more selected channels from the multiple acquisitionchannels for further processing based at least in part on the comparisonof the determined parameters; and process retained frames of the MRSspectral acquisition series from the one or more selected channels togenerate the processed MRS spectrum.
 12. The MRS processing system ofclaim 10, wherein the computer-readable instructions are configured tocause the one or more computer processors to: identify a spectralfeature in spectra for the frames of the MRS spectral acquisitionseries; determine confidence values for the identification of thespectral feature for the frames; determine the set of excluded framesand the set of retained frames based on the determined confidencevalues.
 13. The MRS processing system of claim 10, wherein thecomputer-readable instructions are configured to cause the one or morecomputer processors to determine the set of excluded frames and the setof retained frames based at least in part on a determined confidence ina calculated frequency shift error.
 14. The MRS processing system ofclaim 10, wherein the computer-readable instructions are configured tocause the one or more computer processors to: determine that the numberof retained frames is less than a minimum threshold number; and apply adifferent frame editing criterion to change one or more frames from theset of excluded frames to the set of retained frames so that the numberof retained frames satisfies the minimum threshold number.
 15. The MRSprocessing system of claim 10, wherein the ROI includes at least aportion of an intervertebral disc of a subject.
 16. A magnetic resonancespectroscopy (MRS) processing system comprising: one or more computerprocessors; and one or more non-transitory computer readable mediacomprising computer-readable instructions configured to cause the one ormore computer processors to: receive an MRS spectral acquisition seriesthat includes a set of free induction decay (FID) frames generated andacquired from a voxel located within a region of interest (ROI) via anMRS pulse sequence operation of an MRS system; identify a spectralfeature in spectra for one or more of the frames of the MRS spectralacquisition series; shift the spectra of one or more of the frames ofthe MRS spectral acquisition series based at least in part on theidentified spectral feature to align the identified spectral featureacross the frames; and process at least the shifted frames of the MRSspectral acquisition series to generate a processed MRS spectrum. 17.The MRS processing system of claim 16, wherein the computer-readableinstructions are configured to cause the one or more computer processorsto determine frequency shift error based at least in part on theidentified spectral feature for the one or more frames of the MRSspectral acquisition series, and wherein the shifting of the spectra ofthe one or more frames corrects for the frequency shift error.
 18. TheMRS processing system of claim 17, wherein the computer-readableinstructions are configured to cause the one or more computer processorsto: determine confidence values for the determined frequency shifterrors; determine which of the frames to excluded and which frames toinclude as retained frames based at least in part on the determinedconfidence values; and process the retrained frames of the MRS spectralacquisition series to generate the processed MRS spectrum.
 19. The MRSprocessing system of claim 16, wherein the spectral feature comprises awater signal.
 20. The MRS processing system of claim 16, wherein the ROIincludes at least a portion of an intervertebral disc of a subject.